Wire-in-needle versus conventional syringe-on-needle technique for ultrasound-guided central venous catheter insertion in the internal jugular vein: the WIN randomized trial.

Purpose: There are different techniques for ultrasound-guided central venous catheter (CVC) insertion. When using the conventional syringe-on-needle technique, the syringe needs to be removed from the needle after venous puncture to pass the guidewire through the needle into the vein. When, alternatively, using the wire-in-needle technique, the needle is preloaded with the guidewire, and the guidewire-after venous puncture-is advanced into the vein under real-time ultrasound guidance.

Data-informed reservoir computing for efficient time-series prediction.

We propose a new approach to dynamical system forecasting called data-informed-reservoir computing (DI-RC) that, while solely being based on data, yields increased accuracy, reduced computational cost, and mitigates tedious hyper-parameter optimization of the reservoir computer (RC). Our DI-RC approach is based on the recently proposed hybrid setup where a knowledge-based model is combined with a machine learning prediction system, but it replaces the knowledge-based component by a data-driven model discovery technique. As a result, our approach can be chosen when a suitable knowledge-based model is not available.

Master Memory Function for Delay-Based Reservoir Computers With Single-Variable Dynamics.

We show that many delay-based reservoir computers considered in the literature can be characterized by a universal master memory function (MMF). Once computed for two independent parameters, this function provides linear memory capacity for any delay-based single-variable reservoir with small inputs. Moreover, we propose an analytical description of the MMF that enables its efficient and fast computation.

Deriving task specific performance from the information processing capacity of a reservoir computer.

In the reservoir computing literature, the information processing capacity is frequently used to characterize the computing capabilities of a reservoir. However, it remains unclear how the information processing capacity connects to the performance on specific tasks. We demonstrate on a set of standard benchmark tasks that the total information processing capacity correlates poorly with task specific performance.

Deep time-delay reservoir computing: Dynamics and memory capacity.

The deep time-delay reservoir computing concept utilizes unidirectionally connected systems with time-delays for supervised learning. We present how the dynamical properties of a deep Ikeda-based reservoir are related to its memory capacity (MC) and how that can be used for optimization. In particular, we analyze bifurcations of the corresponding autonomous system and compute conditional Lyapunov exponents, which measure generalized synchronization between the input and the layer dynamics.