Gartner released its 2020 technology trends last month with great – and appropriate – fanfare. Before I comment, we should all note just how volatile the technology world has become. So let me say at the outset that I appreciate Gartner's galvanizing a ton of trends into coherence. The robotic process automation (RPA) revolution is already in full swing. It's hard to find a company not looking at processes it can automate, and it's harder to find a company not keenly aware of technological leverage in the RPA mission.

Trained AI agents can adopt human design strategies to solve problems, according to findings published in the ASME Journal of Mechanical Design. Big design problems require creative and exploratory decision making, a skill in which humans excel. When engineers use artificial intelligence (AI), they have traditionally applied it to a problem within a defined set of rules rather than having it generally follow human strategies to create something new. This novel research considers an AI framework that learns human design strategies through observation of human data to generate new designs without explicit goal information, bias, or guidance. The study was co-authored by Jonathan Cagan, professor of mechanical engineering and interim dean of Carnegie Mellon University's College of Engineering, Ayush Raina, a Ph.D. candidate in mechanical engineering at Carnegie Mellon, and Chris McComb, an assistant professor of engineering design at the Pennsylvania State University.

Artificial intelligence can be used to accurately examine electrocardiogram (ECG) test results, according to the findings of two preliminary studies to be presented at the American Heart Association's Scientific Sessions 2019 November 16-18 in Philadelphia. In the first study, researchers evaluated 1.1 million ECGs that indicated atrial fibrillation (AF) from more than 237,000 patients. They used specialized computational hardware to train a deep neutral network to assess 30,000 data points for each respective ECG. The results showed that approximately one in three people received an AF diagnosis within a year. Moreover, the model demonstrated the capacity for long-term prognostic significance as patients predicted to develop AF after one year had a 45% higher hazard rate in developing AF over a follow-up duration of 25-years compared to other patients.

The idea of "home" has greatly evolved throughout the millennia. Over the decades, technology has transformed homes into hubs of functionality -- centers of entertainment, work, fitness, security, climate control, and more. Home-based technologies are undergoing a rapid evolution thanks to new Internet of Things (IoT) solutions and next-generation IoT solutions currently in development will further transform our homes. While some may view the connected home as a futuristic concept, it is already a reality, as the hurdles to achieving connectivity have largely been met. While adoption of connected home technologies continues to expand, the next phase of innovation will guide the transition to homes that are more proactive and automated than we can currently imagine.

CNR-IAC, Rome, Italy Abstract Turbulence, the ubiquitous and chaotic state of fluid motions, is characterized by strong and statistically nontrivial fluctuations of the velocity field, over a wide range of length-and timescales, and it can be quantitatively described only in terms of statistical averages. Strong non-stationarities hinder the possibility to achieve statistical convergence, making it impossible to define the turbulence intensity and, in particular, its basic dimensionless estimator, the Reynolds number. Here we show that by employing Deep Neural Networks (DNN) we can accurately estimate the Reynolds number within 15% accuracy, from a statistical sample as small as two large-scale eddy-turnover times. In contrast, physics-based statistical estimators are limited by the rate of convergence of the central limit theorem, and provide, for the same statistical sample, an error at least 100 times larger. Our findings open up new perspectives in the possibility to quantitatively define and, therefore, study highly non-stationary turbulent flows as ordinarily found in nature as well as in industrial processes. Turbulence is characterized by complex statistics of velocity fluctuations correlated over a wide range of temporal-and spatial-scales.

Geophysics Department, Stanford University, Stanford, California, USA In this study we develop a single-station deep-learning approach for fast and reliable estimation of earthquake magnitude directly from raw waveforms. We design a regressor composed of convolutional and recurrent neural networks that is not sensitive to the data normalization, hence waveform amplitude information can be utilized during the training. Our network can predict earthquake magnitudes with an average error close to zero and standard deviation of 0.2 based on single-station waveforms without instrument response correction. We test the network for both local and duration magnitude scales and show a station-based learning can be an effective approach for improving the performance. The proposed approach has a variety of potential applications from routine earthquake monitoring to early warning systems.

As the cornerstone of modern portfolio theory, Markowitz's mean-variance optimization is considered a major model adopted in portfolio management. However, due to the difficulty of estimating its parameters, it cannot be applied to all periods. In some cases, naive strategies such as Equally-weighted and Value-weighted portfolios can even get better performance. Under these circumstances, we can use multiple classic strategies as multiple strategic arms in multi-armed bandit to naturally establish a connection with the portfolio selection problem. This can also help to maximize the rewards in the bandit algorithm by the trade-off between exploration and exploitation. In this paper, we present a portfolio bandit strategy through Thompson sampling which aims to make online portfolio choices by effectively exploiting the performances among multiple arms. Also, by constructing multiple strategic arms, we can obtain the optimal investment portfolio to adapt different investment periods. Moreover, we devise a novel reward function based on users' different investment risk preferences, which can be adaptive to various investment styles. Our experimental results demonstrate that our proposed portfolio strategy has marked superiority across representative real-world market datasets in terms of extensive evaluation criteria.

We present a simulation framework for spunbond processes and use a design of experiments to investigate the cause-and-effect-relations of process and material parameters onto the fiber laydown on a conveyor belt. The virtual experiments are analyzed by a blocked neural network. This forms the basis for the prediction of the fiber laydown characteristics and enables a quick ranking of the significance of the influencing effects. We conclude our research by an analysis of the nonlinear cause-and-effect relations.

--Ensuring secure and reliable operations of the power grid is a primary concern of system operators. Phasor measurement units (PMUs) are rapidly being deployed in the grid to provide fast-sampled operational data that should enable quicker decision-making. This work presents a general interpretable framework for analyzing real-time PMU data, and thus enabling grid operators to understand the current state and to identify anomalies on the fly. Applying statistical learning tools on the streaming data, we first learn an effective dynamical model to describe the current behavior of the system. Next, we use the probabilistic predictions of our learned model to define in a principled way an efficient anomaly detection tool. Finally, the last module of our framework produces on-the-fly classification of the detected anomalies into common occurrence classes using features that grid operators are familiar with. We demonstrate the efficacy of our interpretable approach through extensive numerical experiments on real PMU data collected from a transmission operator in the USA. Traditional supervisory control and data acquisition (SCADA) systems provide information regarding the system state at the order of seconds to the operator. However, such fidelity, considered appropriate in prior decades, is not sufficient to observe or predict disturbances at faster timescales that are increasingly being observed in today's stochastic grid [1]. To provide more rapid measurement data, phasor measurement units (PMUs) have gained widespread deployment. PMUs [2] are time-synchronized by GPS timestamps and collect measurements of system states (Eg.

November 15, 2019 Abstract Here, we provide a supplementary material for Takayuki Osogami, "Uncorrected least-squares temporal difference with lambda-return," which appears in Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI-20) [Osogami, 2019]. A Proofs In this section, we prove Theorem 1, Lemma 1, Theorem 2, Lemma 2, and Proposition 1. Note that equations (1)-(19) refers to those in Osogami [2019]. A.1 Proof of Theorem 1 From (7)-(8), we have the following equality: A Unc T 1 T null t 0φ t null φ t (1 λ) γ T t null m 1( λγ) m 1 φ t mnull null (20) T 1 null t 0φ t null φ t (1 λ) γ T t null m 1(λγ) m 1 φ t mnull null φ T φ null T (21) T 1 null t 0φ tnull φ t (1 λ) γ T t 1 null m 1(λγ) m 1 φ t m (1 λ) γ (λγ) T t 1 φ Tnull null φ T φ null T (22) A Unc T T 1 null t 0φ t(1 λ) γ (λγ) T t 1 φ null T φ T φ null T (23) A Unc T null T null t 0(λγ) T t φ tnull φ null T γ null T 1 null t 0( λγ) T t 1 φ tnull φ null T (24) A Unc T ( z T γ z T 1) φ null T . The recursive computation of the eligibility trace can be verified in a straightforward manner.