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Standards and recommendations for transitioning organizations to quantum-secure cryptographic protocols are outlined, including a discussion of transition timelines and the leading strategies to protect systems against quantum attacks.
A dandelion-inspired wireless solar-powered sensing device weighing 30 milligrams that transmits data through radio backscatter achieves dispersal over a wide area by travelling on the breeze, and successfully lands upright.
Using the game Gran Turismo, an agent was trained with a combination of deep reinforcement learning algorithms and specialized training scenarios, demonstrating success against championship-level human racers.
A hybrid algorithm that applies backpropagation is used to train layers of controllable physical systems to carry out calculations like deep neural networks, but accounting for real-world noise and imperfections.
An analysis of three surveys of COVID-19 vaccine behaviour shows that larger surveys overconfidently overestimated vaccine uptake, a demonstration of how larger sample sizes can paradoxically lead to less accurate estimates.
A framework through which machine learning can guide mathematicians in discovering new conjectures and theorems is presented and shown to yield mathematical insight on important open problems in different areas of pure mathematics.
Mapping of the global potential of atmospheric water harvesting using solar energy shows that it could provide safely managed drinking water for a billion people worldwide based on climate suitability.
Modelling highlights international travel as the main driver of the introduction of SARS-CoV-2 to Europe and the USA, and suggests that introductions and local transmission may have begun in January 2020.
A biologically informed, interpretable deep learning model has been developed to evaluate molecular drivers of resistance to cancer treatment, predict clinical outcomes and guide hypotheses on disease progression.
Principles from the field of fair division are used to develop selection algorithms for citizens’ assemblies that produce panels that are representative of the population while simultaneously selecting individuals with near-equal probabilities.
Machine learning tools are used to greatly accelerate chip layout design, by posing chip floorplanning as a reinforcement learning problem and using neural networks to generate high-performance chip layouts.
This Perspective discusses how high-energy-density physics could tap the potential of AI-inspired algorithms for extracting relevant information and how data-driven automatic control routines may be used for optimizing high-repetition-rate experiments.
A deep-learning-based algorithm uses routinely acquired histology slides to provide a differential diagnosis for the origin of the primary tumour for complicated cases of metastatic tumours and cancers of unknown primary origin.
The pressure dependence and magnetic field dependence of the specific heat of a quantum magnet, SrCu2(BO3)2, demonstrate that its phase diagram contains a line of first-order transitions terminating at a critical point, in analogy with water.
A theoretical study of non-reciprocity in collective phenomena reveals the emergence of time-dependent phases heralded by exceptional points in contexts ranging from synchronization and flocking to pattern formation.
A deep-learning-based approach using a convolutional neural network is used to synthesize photorealistic colour three-dimensional holograms from a single RGB-depth image in real time, and termed tensor holography.
A reinforcement learning algorithm that explicitly remembers promising states and returns to them as a basis for further exploration solves all as-yet-unsolved Atari games and out-performs previous algorithms on Montezuma’s Revenge and Pitfall.
Bayesian optimization is applied in chemical synthesis towards the optimization of various organic reactions and is found to outperform scientists in both average optimization efficiency and consistency.
An integrated photonic processor, based on phase-change-material memory arrays and chip-based optical frequency combs, which can operate at speeds of trillions of multiply-accumulate (MAC) operations per second, is demonstrated.
A reinforcement-learning algorithm that combines a tree-based search with a learned model achieves superhuman performance in high-performance planning and visually complex domains, without any knowledge of their underlying dynamics.
Data augmentation and a self-correcting design are used to develop a reinforcement-learning algorithm for the autonomous navigation of Loon superpressure balloons in challenging stratospheric weather conditions.
An epidemiological model that integrates fine-grained mobility networks illuminates mobility-related mechanisms that contribute to higher infection rates among disadvantaged socioeconomic and racial groups, and finds that restricting maximum occupancy at locations is especially effective for curbing infections.
NumPy is the primary array programming library for Python; here its fundamental concepts are reviewed and its evolution into a flexible interoperability layer between increasingly specialized computational libraries is discussed.
Breakthroughs in artificial intelligence and low-cost, contactless sensors have given rise to an ambient intelligence that can potentially improve the physical execution of healthcare delivery, if used in a thoughtful manner.
A video-based deep learning algorithm—EchoNet-Dynamic—accurately identifies subtle changes in ejection fraction and classifies heart failure with reduced ejection fraction using information from multiple cardiac cycles.
A theoretical framework describing the hydrodynamic interactions between a passive particle and an active medium in out-of-equilibrium systems predicts long-range Lévy flights for the diffusing particle driven by the density of the active component.
A closed-loop machine learning methodology of optimizing fast-charging protocols for lithium-ion batteries can identify high-lifetime charging protocols accurately and efficiently, considerably reducing the experimental time compared to simpler approaches.
A probabilistic computer utilizing probabilistic bits, or p-bits, is implemented with stochastic nanomagnetic devices in a neural-network-inspired electrical circuit operating at room temperature and demonstrates integer factorization up to 945.
Human scientists make unrepresentative chemical reagent and reaction condition choices, and machine-learning algorithms trained on human-selected experiments are less capable of successfully predicting reaction outcomes than those trained on randomly generated experiments.