The sustainability of modern cities highly depends on efficient water distribution management, including effective pressure control and leak detection and localization. Accurate information about the network hydraulic state is therefore essential. This article presents a comparison between two data-driven state estimation methods based on the Unscented Kalman Filter (UKF), fusing pressure, demand and flow data for head and flow estimation. One approach uses a joint state vector with a single estimator, while the other uses a dual-estimator scheme. We analyse their main characteristics, discussing differences, advantages and limitations, and compare them theoretically in terms of accuracy and complexity. Finally, we show several estimation results for the L-TOWN benchmark, allowing to discuss their properties in a real implementation.

The intrinsic high dimension of fluid dynamics is an inherent challenge to control of aerodynamic flows, and this is further complicated by a flow's nonlinear response to strong disturbances. Deep reinforcement learning, which takes advantage of the exploratory aspects of reinforcement learning (RL) and the rich nonlinearity of a deep neural network, provides a promising approach to discover feasible control strategies. However, the typical model-free approach to reinforcement learning requires a significant amount of interaction between the flow environment and the RL agent during training, and this high training cost impedes its development and application. In this work, we propose a model-based reinforcement learning (MBRL) approach by incorporating a novel reduced-order model as a surrogate for the full environment. The model consists of a physics-augmented autoencoder, which compresses high-dimensional CFD flow field snaphsots into a three-dimensional latent space, and a latent dynamics model that is trained to accurately predict the long-time dynamics of trajectories in the latent space in response to action sequences. The robustness and generalizability of the model is demonstrated in two distinct flow environments, a pitching airfoil in a highly disturbed environment and a vertical-axis wind turbine in a disturbance-free environment. Based on the trained model in the first problem, we realize an MBRL strategy to mitigate lift variation during gust-airfoil encounters. We demonstrate that the policy learned in the reduced-order environment translates to an effective control strategy in the full CFD environment.

In this paper, we propose modelling human translation production as a hierarchy of three embedded translation processes. The proposed architecture replicates the temporal dynamics of keystroke production across sensorimotor, cognitive, and phenomenal layers. Utilizing data from the CRITT TPR-DB, the Task Segment Framework, and the HOF taxonomy, we demonstrate the temporal breakdown of the typing flow on distinct timelines within these three layers.

Turbulence is characterised by chaotic dynamics and a high-dimensional state space, which make this phenomenon challenging to predict. However, turbulent flows are often characterised by coherent spatiotemporal structures, such as vortices or large-scale modes, which can help obtain a latent description of turbulent flows. However, current approaches are often limited by either the need to use some form of thresholding on quantities defining the isosurfaces to which the flow structures are associated or the linearity of traditional modal flow decomposition approaches, such as those based on proper orthogonal decomposition. This problem is exacerbated in flows that exhibit extreme events, which are rare and sudden changes in a turbulent state. The goal of this paper is to obtain an efficient and accurate reduced-order latent representation of a turbulent flow that exhibits extreme events. Specifically, we employ a three-dimensional multiscale convolutional autoencoder (CAE) to obtain such latent representation. We apply it to a three-dimensional turbulent flow. We show that the Multiscale CAE is efficient, requiring less than 10% degrees of freedom than proper orthogonal decomposition for compressing the data and is able to accurately reconstruct flow states related to extreme events. The proposed deep learning architecture opens opportunities for nonlinear reduced-order modeling of turbulent flows from data.

The dynamics of a turbulent flow tend to occupy only a portion of the phase space at a statistically stationary regime. From a dynamical systems point of view, this portion is the attractor. The knowledge of the turbulent attractor is useful for two purposes, at least: (i) We can gain physical insight into turbulence (what is the shape and geometry of the attractor?), and (ii) it provides the minimal number of degrees of freedom to accurately describe the turbulent dynamics. Autoencoders enable the computation of an optimal latent space, which is a low-order representation of the dynamics. If properly trained and correctly designed, autoencoders can learn an approximation of the turbulent attractor, as shown by Doan, Racca and Magri (2022). In this paper, we theoretically interpret the transformations of an autoencoder. First, we remark that the latent space is a curved manifold with curvilinear coordinates, which can be analyzed with simple tools from Riemann geometry. Second, we characterize the geometrical properties of the latent space. We mathematically derive the metric tensor, which provides a mathematical description of the manifold. Third, we propose a method -- proper latent decomposition (PLD) -- that generalizes proper orthogonal decomposition of turbulent flows on the autoencoder latent space. This decomposition finds the dominant directions in the curved latent space. This theoretical work opens up computational opportunities for interpreting autoencoders and creating reduced-order models of turbulent flows.

A number of academics and tech entrepreneurs agree: computer intelligence will one day meet and exceed human intelligence. But almost none of them agree on what happens after that. Depending on who you ask, it could be the end of the world or the greatest period of human prosperity we've ever known. I am squarely in the prosperity camp, but Hollywood lends a lot of momentum to those in the doomsday camp. Movies like Terminator, Transcendence, and The Matrix share the archetypal plot point that machines will enslave or kill mankind.

Artificial Intelligence at the Digital Workplace – Physiolytics for Flow-aware Notifications Digital technologies enabling the delivery of information in the form of real-time notifications are the foundation of today's digital workplace. Beside all its potential, digital technologies also lead to interruptions at work. Thus, designing human-centric intelligent digital notifications is becoming an important challenge. Flow, a state in which individual employees are completely absorbed and highly concentrated when performing a task was first investigated by psychologist Mihaly Csikszentmihalyi and it has been shown that flow states can in turn lead to a higher level of well-being, satisfaction or performance of the employee. To date, however, researchers have relied primarily on self-reported scales when measuring flow.

A number of academics and tech entrepreneurs agree: computer intelligence will one day meet and exceed human intelligence. But almost none of them agree on what happens after that. Depending on who you ask, it could be the end of the world or the greatest period of human prosperity we've ever known. I am squarely in the prosperity camp, but Hollywood lends a lot of momentum to those in the doomsday camp. Movies like Terminator, Transcendence, and The Matrix share the archetypal plot point that machines will enslave or kill mankind.

Can a video game be more than just a game? Can it train you to focus? To disassociate yourself from traumatic memories and heal your mind? Can it transcend your personal experience and bridge a geopolitical divide? These aren't just ridiculous claims from a marketer's fever dream -- one video game has done all of this before, reaching hundreds of millions of players: Tetris.

Dreaming of sheep or hacking creativity for abundance?Depositphotos enhanced by CogWorld "We now live in a global, exponential world," Steven Kotler tells my coauthor Michael Ashley and I from his Santa Monica office. We're interviewing the New York Times bestselling author and entrepreneur for our upcoming book: Uber Yourself Before You Get Kodaked: A Modern Primer on A.I. for the Modern Business. "You need to understand our brains evolved in a local, linear environment. But in the 21st century, according to research done by Ray Kurzweil, we will experience over 20,000 years' worth of change. To put it succinctly, over the next 80-something years we will go through the birth of agriculture to the industrial revolution -- twice -- in terms of our technological advancement."