Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to classic neural network structures, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN), and reinforcement learning. Application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation will be covered. High-Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction to mathematical foundations. Readers will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this book; however, familiarity with at least one programming language is assumed.

This paper presents a novel online framework for safe crowd-robot interaction based on risk-sensitive stochastic optimal control, wherein the risk is modeled by the entropic risk measure. The sampling-based model predictive control relies on mode insertion gradient optimization for this risk measure as well as Trajectron++, a state-of-the-art generative model that produces multimodal probabilistic trajectory forecasts for multiple interacting agents. Our modular approach decouples the crowd-robot interaction into learning-based prediction and model-based control, which is advantageous compared to end-to-end policy learning methods in that it allows the robot's desired behavior to be specified at run time. In particular, we show that the robot exhibits diverse interaction behavior by varying the risk sensitivity parameter. A simulation study and a real-world experiment show that the proposed online framework can accomplish safe and efficient navigation while avoiding collisions with more than 50 humans in the scene.

Multivariate time series (MTS) forecasting is an important problem in many fields. Accurate forecasting results can effectively help decision-making. Variables in MTS have rich relations among each other and the value of each variable in MTS depends both on its historical values and on other variables. These rich relations can be static and predictable or dynamic and latent. Existing methods do not incorporate these rich relational information into modeling or only model certain relation among MTS variables. To jointly model rich relations among variables and temporal dependencies within the time series, a novel end-to-end deep learning model, termed Multivariate Time Series Forecasting via Heterogenous Graph Neural Networks (MTHetGNN) is proposed in this paper. To characterize rich relations among variables, a relation embedding module is introduced in our model, where each variable is regarded as a graph node and each type of edge represents a specific relationship among variables or one specific dynamic update strategy to model the latent dependency among variables. In addition, convolutional neural network (CNN) filters with different perception scales are used for time series feature extraction, which is used to generate the feature of each node. Finally, heterogenous graph neural networks are adopted to handle the complex structural information generated by temporal embedding module and relation embedding module. Three benchmark datasets from the real world are used to evaluate the proposed MTHetGNN and the comprehensive experiments show that MTHetGNN achieves state-of-the-art results in MTS forecasting task.

Our planet vibrates incessantly, sometimes with notable but more often with imperceptible intensity. Conventional seismology attempts to decipher vibrational sources and path effects by studying seismograms—records of vibrations measured with seismometers. In doing so, scientists seek either to understand the tectonic processes that lead to strong ground motions and earthquake failure ([ 1 ][1]) or to probe otherwise inaccessible planetary interiors ([ 2 ][2]). Progress in these areas of research typically has relied on the rare and geographically irregular occurrence of large earthquakes. However, anthropogenic (human) activities at Earth's surface also generate seismic waves that instruments can detect over great distances. On page 1338 of this issue, Lecocq et al. ([ 3 ][3]) report on a quieting of anthropogenic vibrations since the start of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Seismology has benefited from a surge in seismic data volume, computational power, and corresponding methodological development. These advances have enabled seismologists to branch away from traditional source and subsurface characterization of the energy from earthquakes and human-made blasts. The expansion of seismic networks has allowed the observation of previously unseen natural processes as diverse as wildlife activity ([ 4 ][4]), bed load transport in rivers, glacier sliding ([ 5 ][5]), and surface-mass wasting ([ 6 ][6]). In particular, scientists use continuous, ambient seismic vibrations to probe volcanic activities ([ 7 ][7]) and groundwater resources ([ 8 ][8]), to track storms ([ 9 ][9]), and to decipher ice sheet processes ([ 10 ][10]). Human cultural noise carries seismic signatures mostly at frequencies above 1 Hz, whether the source is transient (entertainment; individual cars, trains, or planes), harmonic (wind turbines, machinery), or diffuse (railroads, highways) ([ 11 ][11], [ 12 ][12]) (see the figure). Overall, anthropogenic seismic noise levels have increased over the past few decades, and there is a clear positive correlation between this increase and gross domestic product ([ 13 ][13]). But when the SARS-CoV-2 pandemic began to ravage the planet, humans—and Earth—went quiet. Through a global analysis of seismic noise levels, Lecocq et al. found that most sites experienced a drastic reduction in noise levels in the 4- to 14-Hz frequency band. This reduction was much greater than those observed during the annual noise-level cycles of national or religious holidays. Daily CO2 emissions fell only 11 to 25% ([ 14 ][14]), whereas anthropogenic vibrations dropped by 75% in most countries that imposed lockdown measures. Among countries with the greatest noise reductions were China, Italy, and France—all densely populated places with strong government responses (that is, with high virus-containment indices) ([ 15 ][15]). Lecocq et al. also detected a correlation between seismic data and new types of time series, such as urban audible sound from acoustics data and cell phone mobility data. The authors observed the greatest correlations between seismic noise levels and two common types of pandemic mitigation: surface transportation and nonessential business activities. Lecocq et al. did not detect a strong correlation between lockdown and seismic noise reduction at other frequency bands, which might be explained by certain uninterrupted human activities such as power generation ([ 14 ][14]). For all its hardships, the lockdown has unlocked a door to scientific inquiry into environmental noise and global collaboration. At a fundamental level, low noise benefits traditional seismology, hence the recent noise decrease might open new windows of opportunity; study areas hindered by urban noise might now be targets for detecting microseismicity or for improved subsurface imaging. The crucial next step, as ever in seismology, is to determine the causative nature of these signals beyond their correlation—thus turning anthropogenic noise into informative signals that allow scientists to address new questions. For example: Is there feedback between anthropogenic vibrations and Earth processes? And will seismic monitoring of anthropogenic and environmental activities become complementary, economically valuable alternatives to conventional techniques? To achieve these advances, seismologists must develop new ways of processing data and modeling and interpreting results. Lecocq et al. exemplify seismological progress through best practices in scientific research: public data, open-access software and hardware, global cooperation, and crowdsourcing of citizen-science projects. All of the data are publicly available through open-access data centers at the Incorporated Research Institutions for Seismology (IRIS), which hosts and redistributes real-time seismograms from most of the stations participating in the Federation of Digital Seismograph Networks archive. A large proportion of the data used in the Lecocq et al. study was measured on seismic instruments that are powered on open-source Raspberry Pi computers hosted by citizen scientists. The Raspberry Shake network counts more than 3500 stations globally, all installed in homes, schools, and research institutions at 2 to 7% of the cost of conventional research or industrial sensors. The authors performed data analyses with open-source Python software Obspy, demonstrating the prevalence and usefulness of open-source community codes in modern science. Like the pandemic, the seismological community also is shaking up norms. One important example is the reorganization of research activities. Although physical borders are closed, Lecocq et al. demonstrate that, much like the global medical research on SARS-CoV-2, seismological research is and ought to be without borders. The new study represents scientists from 25 countries on five continents, and the authors shared the manuscript on public editing platforms (Google Docs, Slack) that allowed for all members of the community to contribute. Indeed, social seismology, which directly relates human activities and seismic waves, has sparked enthusiasm in the scientific community for urban seismology. The fall meeting of the American Geophysical Union (December 2020) will highlight the imminent wave of SARS-CoV-2–related seismological science in a special session called “Social Seismology.” ![Figure][16] Humans and nature excite seismic waves Seismometers record vibrations from everything, not only earthquakes. Shown are sources that induce seismic waves of different vibration modes (harmonic, diffuse, transient), detectable over large distances. GRAPHIC: N. DESAI/ SCIENCE 1. [↵][17]1. M. A. Denolle, 2. E. M. Dunham, 3. G. A. Prieto, 4. G. C. Beroza , Science 343, 399 (2014). [OpenUrl][18][Abstract/FREE Full Text][19] 2. [↵][20]1. K. Hosseini et al ., Geophys. J. Int. 220, 96 (2020). [OpenUrl][21] 3. [↵][22]1. T. Lecocq et al ., Science 369, 1338 (2020). [OpenUrl][23][CrossRef][24][PubMed][25] 4. [↵][26]1. B. Mortimer, 2. W. L. Rees, 3. P. Koelemeijer, 4. T. Nissen-Meyer , Curr. Biol. 28, R547 (2018). [OpenUrl][27][CrossRef][28] 5. [↵][29]1. E. A. Podolskiy, 2. F. Walter , Rev. 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But the potential for 5G in business leaves plenty of room for excitement, too, and organizations should also start thinking about how 5G could improve processes and production. The time to dream is now.

Many modern applications involve the acquisition of noisy modulo samples of a function $f$, with the goal being to recover estimates of the original samples of $f$. For a Lipschitz function $f:[0,1]^d \to \mathbb{R}$, suppose we are given the samples $y_i = (f(x_i) + \eta_i)\bmod 1; \quad i=1,\dots,n$ where $\eta_i$ denotes noise. Assuming $\eta_i$ are zero-mean i.i.d Gaussian's, and $x_i$'s form a uniform grid, we derive a two-stage algorithm that recovers estimates of the samples $f(x_i)$ with a uniform error rate $O((\frac{\log n}{n})^{\frac{1}{d+2}})$ holding with high probability. The first stage involves embedding the points on the unit complex circle, and obtaining denoised estimates of $f(x_i)\bmod 1$ via a $k$NN (nearest neighbor) estimator. The second stage involves a sequential unwrapping procedure which unwraps the denoised mod $1$ estimates from the first stage. Recently, Cucuringu and Tyagi proposed an alternative way of denoising modulo $1$ data which works with their representation on the unit complex circle. They formulated a smoothness regularized least squares problem on the product manifold of unit circles, where the smoothness is measured with respect to the Laplacian of a proximity graph $G$ involving the $x_i$'s. This is a nonconvex quadratically constrained quadratic program (QCQP) hence they proposed solving its semidefinite program (SDP) based relaxation. We derive sufficient conditions under which the SDP is a tight relaxation of the QCQP. Hence under these conditions, the global solution of QCQP can be obtained in polynomial time.

Beginning with AI means you need a proper data culture to start with. AI is not magic, despite what many may still think. Before even thinking of AI, the data needs to be in order. You need documentation, policies, and most importantly a proper data culture. This is the first in a series of interviews with practitioners in the field about generating business value with AI.

If I were to picture futuristic bots that could revolutionize both microrobotics and medicine, a Pop-Tart with four squiggly legs would not be on top of my list. Last week, Drs. Marc Miskin*, Itai Cohen, and Paul McEuen at Cornell University spearheaded a collaboration that tackled one of the most pressing problems in microrobotics--getting those robots to move in a controllable manner. They graced us with an army of Pop-Tart-shaped microbots with seriously tricked-out actuators, or motors that allow a robot to move. In this case, the actuators make up the robot's legs. Each smaller than the width of a human hair, the bots have a blocky body equipped with solar cells and two pairs of platinum legs, which can be independently triggered to flex using precise laser zaps.

In this article, to answer these questions, we go through the Monte Carlo Tree Search fundamentals. Since in the next articles, we will implement this algorithm on "HEX" board game, I try to explain the concepts through examples in this board game environment. If you're more interested in the code, find it in this link. There is also a more optimized version which is applicable on linux due to utilizing cython and you can find it in here. Monte Carlo method was coined by Stanislaw Ulam for the first time after applying statistical approach "The Monte Carlo method".

Civil and maritime engineering systems, among others, from bridges to offshore platforms and wind turbines, must be efficiently managed as they are exposed to deterioration mechanisms throughout their operational life, such as fatigue or corrosion. Identifying optimal inspection and maintenance policies demands the solution of a complex sequential decision-making problem under uncertainty, with the main objective of efficiently controlling the risk associated with structural failures. Addressing this complexity, risk-based inspection planning methodologies, supported often by dynamic Bayesian networks, evaluate a set of pre-defined heuristic decision rules to reasonably simplify the decision problem. However, the resulting policies may be compromised by the limited space considered in the definition of the decision rules. Avoiding this limitation, Partially Observable Markov Decision Processes (POMDPs) provide a principled mathematical methodology for stochastic optimal control under uncertain action outcomes and observations, in which the optimal actions are prescribed as a function of the entire, dynamically updated, state probability distribution. In this paper, we combine dynamic Bayesian networks with POMDPs in a joint framework for optimal inspection and maintenance planning, and we provide the formulation for developing both infinite and finite horizon POMDPs in a structural reliability context. The proposed methodology is implemented and tested for the case of a structural component subject to fatigue deterioration, demonstrating the capability of state-of-the-art point-based POMDP solvers for solving the underlying planning optimization problem. Within the numerical experiments, POMDP and heuristic-based policies are thoroughly compared, and results showcase that POMDPs achieve substantially lower costs as compared to their counterparts, even for traditional problem settings.