Stochastic Syncing in Sinusoidally Driven Atomic Orbital Memory.

Stochastically fluctuating multiwell systems are a promising route toward physical implementations of energy-based machine learning and neuromorphic hardware. One of the challenges is finding tunable material platforms that exhibit such multiwell behavior and understanding how complex dynamic input signals influence their stochastic response. One such platform is the recently discovered atomic Boltzmann machine, where each stochastic unit is represented by a binary orbital memory state of an individual atom.

Postoperative Delirium After Intracranial Surgery: A Retrospective Cohort Study.

Background: The clinical relevance of postoperative delirium (POD) in neurosurgery remains unclear and should be investigated because these patients are vulnerable. Hence, we investigated the impact of POD, by means of incidence and health outcomes, and identified independent risk factors.

Methods: Adult patients undergoing an intracranial surgical procedure in the Erasmus Medical Center Rotterdam between June 2017 and September 2020 were retrospectively included.

An atomic Boltzmann machine capable of self-adaption.

The quest to implement machine learning algorithms in hardware has focused on combining various materials, each mimicking a computational primitive, to create device functionality. Ultimately, these piecewise approaches limit functionality and efficiency, while complicating scaling and on-chip learning, necessitating new approaches linking physical phenomena to machine learning models. Here, we create an atomic spin system that emulates a Boltzmann machine directly in the orbital dynamics of one well-defined material system.

Minimal navigation solution for a swarm of tiny flying robots to explore an unknown environment.

Swarms of tiny flying robots hold great potential for exploring unknown, indoor environments. Their small size allows them to move in narrow spaces, and their light weight makes them safe for operating around humans. Until now, this task has been out of reach due to the lack of adequate navigation strategies.

Bayesian neural networks with variable selection for prediction of genotypic values.

Background: Estimating the genetic component of a complex phenotype is a complicated problem, mainly because there are many allele effects to estimate from a limited number of phenotypes. In spite of this difficulty, linear methods with variable selection have been able to give good predictions of additive effects of individuals. However, prediction of non-additive genetic effects is challenging with the usual prediction methods.