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We demonstrate that Cherenkov radiation can be manipulated in terms of operation frequency, bandwidth, and efficiency by simultaneously controlling the properties of drifting electrons and the photonic states supported by their surrounding media. We analytically show that the radiation rate strongly depends on the momentum of the excited photonic state, in terms of magnitude, frequency dispersion, and its variation vs the properties of the drifting carriers. This approach is applied to design and realize miniaturized, broadband, tunable, and efficient terahertz and far-infrared sources by manipulating and boosting the coupling between drifting electrons and engineered hyperbolic modes in graphene-based nanostructures. The broadband, dispersive, and confined nature of hyperbolic modes relax momentum matching issues, avoid using electron beams, and drastically enhance the radiation rate—allowing that over 90% of drifting electrons emit photons. Our findings open an exciting paradigm for the development of solid-state terahertz and infrared sources. 

We propose engineering optical traps over plasmonic surfaces and precisely controlling the trap position with an external bias by inducing in-plane nonreciprocity on the surface. The platform employs an incident Gaussian beam to polarize targeted nanoparticles, and exploits the interplay between nonreciprocal and spin-orbit lateral recoil forces to construct stable optical traps and manipulate their position within the surface. To model this process, we develop a theoretical framework based on the Lorentz force combined with nonreciprocal Green’s functions and apply it to calculate the trapping potential. Rooted on this formalism, we explore the exciting possibilities offered by graphene to engineer stable optical traps using low-power laser beams in the mid-IR and to manipulate the trap position in a continuous manner by applying a longitudinal drift bias. Nonreciprocal metasurfaces may open new possibilities to trap, assemble and manipulate nanoparticles and overcome many challenges faced by conventional optical tweezers while dealing with nanoscale objects.

 

Paleontological reconstructions of plankton community structure during warm periods of the Cenozoic (last 66 million years) reveal that deep-dwelling ‘twilight zone’ (200–1000 m) plankton were less abundant and diverse, and lived much closer to the surface, than in colder, more recent climates. We suggest that this is a consequence of temperature’s role in controlling the rate that sinking organic matter is broken down and metabolized by bacteria, a process that occurs faster at warmer temperatures. In a warmer ocean, a smaller fraction of organic matter reaches the ocean interior, affecting food supply and dissolved oxygen availability at depth. Using an Earth system model that has been evaluated against paleo observations, we illustrate how anthropogenic warming may impact future carbon cycling and twilight zone ecology. Our findings suggest that significant changes are already underway, and without strong emissions mitigation, widespread ecological disruption in the twilight zone is likely by 2100, with effects spanning millennia thereafter.

 

Large language models have substantially advanced nuance and context understanding in natural language processing (NLP), further fueling the growth of intelligent conversational interfaces and virtual assistants. However, their hefty computational and memory demands make them potentially expensive to deploy on cloudless edge platforms with strict latency and energy requirements. For example, an inference pass using the state-of-the-art BERT-base model must serially traverse through 12 computationally intensive transformer layers, each layer containing 12 parallel attention heads whose outputs concatenate to drive a large feed-forward network. To reduce computation latency, several algorithmic optimizations have been proposed, e.g., a recent algorithm dynamically matches linguistic complexity with model sizes via entropy-based early exit. Deploying such transformer models on edge platforms requires careful co-design and optimizations from algorithms to circuits, where energy consumption is a key design consideration. 

“Fusion” study designs combine data from different sources to answer questions that could not be answered (as well) by subsets of the data. Studies that augment main study data with validation data, as in measurement-error correction studies or generalizability studies, are examples of fusion designs. Fusion estimators, here solutions to stacked estimating functions, produce consistent answers to identified research questions using data from fusion designs. In this paper, we describe a pair of examples of fusion designs and estimators, one where we generalize a proportion to a target population and one where we correct measurement error in a proportion. For each case, we present an example motivated by human immunodeficiency virus research and summarize results from simulation studies. Simulations demonstrate that the fusion estimators provide approximately unbiased results with appropriate 95% confidence interval coverage. Fusion estimators can be used to appropriately combine data in answering important questions that benefit from multiple sources of information.