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The discovery of atomic monolayer magnetic materials has stimulated intense research activities in the two-dimensional (2D) van der Waals (vdW) materials community. The field is growing rapidly and there has been a large class of 2D vdW magnetic compounds with unique properties, which provides an ideal platform to study magnetism in the atomically thin limit. In parallel, based on tunneling magnetoresistance and magneto-optical effect in 2D vdW magnets and their heterostructures, emerging concepts of spintronic and optoelectronic applications such as spin tunnel field-effect transistors and spin-filtering devices are explored. While the magnetic ground state has been extensively investigated, reliable characterization and control of spin dynamics play a crucial role in designing ultrafast spintronic devices. Ferromagnetic resonance (FMR) allows direct measurements of magnetic excitations, which provides insight into the key parameters of magnetic properties such as exchange interaction, magnetic anisotropy, gyromagnetic ratio, spin-orbit coupling, damping rate, and domain structure. In this review article, we present an overview of the essential progress in probing spin dynamics of 2D vdW magnets using FMR techniques. Given the dynamic nature of this field, we focus mainly on broadband FMR, optical FMR, and spin-torque FMR, and their applications in studying prototypical 2D vdW magnets. We conclude with the recent advances in laboratory- and synchrotron-based FMR techniques and their opportunities to broaden the horizon of research pathways into atomically thin magnets. 

The rate at which humanity is producing data has increased sig- nificantly over the last decade. As organizations generate unprece- dented amounts of data, storing, cleaning, integrating, and ana- lyzing this data consumes significant (human and computational) resources. At the same time organizations extract significant value from their data. In this work, we present our vision for develop- ing an objective metric for the value of data based on the recently introduced concept of data relevance, outline proposals for how to efficiently compute and maintain such metrics, and how to utilize data value to improve data management including storage organi- zation, query performance, intelligent allocation of data collection and curation efforts, improving data catalogs, and for making pric- ing decisions in data markets. While we mostly focus on tabular data, the concepts we introduce can also be applied to other data models such as semi-structure data (e.g., JSON) or property graphs. Furthermore, we discuss strategies for dealing with data and work- loads that evolve and discuss how to deal with data that is currently not relevant, but has potential value (we refer to this as dark data). Furthermore, we sketch ideas for measuring the value that a query / workload has for an organization and reason about the interaction between query and data value. 

Modular self-assembling systems typically assume that modules are present to assemble. But in sparsely observed ocean environments, modules of an aquatic modular robotic system may be separated by distances they do not have the energy to cross, and the information needed for optimal path planning is often unavailable. In this work we present a flow-based rendezvous and docking controller that allows aquatic robots in gyre-like environments to rendezvous with and dock to a target by leveraging environmental forces. This approach does not require complete knowledge of the flow, but suffices with imperfect knowledge of the flow's center and shape. We validate the performance of this control approach in both simulations and experiments relative to naive rendezvous and docking strategies, and show that energy efficiency improves as the scale of the gyre increases.