In recent years, the dramatic progress in machine learning has begun to impact many areas of science and technology significantly. In the present perspective article, we explore how quantum technologies are benefiting from this revolution. We showcase in illustrative examples how scientists in the past few years have started to use machine learning and more broadly methods of artificial intelligence to analyze quantum measurements, estimate the parameters of quantum devices, discover new quantum experimental setups, protocols, and feedback strategies, and generally improve aspects of quantum computing, quantum communication, and quantum simulation. We highlight open challenges and future possibilities and conclude with some speculative visions for the next decade.

Across infrastructure domains, physical damage caused by storms and other weather events often requires costly and time-sensitive repairs to restore services as quickly as possible. While recent studies have used agent-based models to estimate the cost of repairs, the implemented strategies for assignment of repair crews to different locations are generally human-driven or based on simple rules. In order to find performant strategies, we continue with an agent-based model, but approach this problem as a combinational optimization problem known as the Minimum Weighted Latency Problem for multiple repair crews. We apply a partitioning algorithm that balances the assignment of targets amongst all the crews using two different heuristics that optimize either the importance of repair locations or the travel time between them. We benchmark our algorithm on both randomly generated graphs as well as data derived from a real-world urban environment, and show that our algorithm delivers significantly better assignments than existing methods.

Random forest (RF) regression model is used to predict the lattice constant, magnetic moment and formation energies of full Heusler alloys, half Heusler alloys, inverse Heusler alloys and quaternary Heusler alloys based on existing as well as indigenously prepared databases. Prior analysis was carried out to check the distribution of the data points of the response variables and found that in most of the cases, the data is not normally distributed. The outcome of the RF model performance is sufficiently accurate to predict the response variables on the test data and also shows its robustness against overfitting, outliers, multicollinearity and distribution of data points. The parity plots between the machine learning predicted values against the computed values using density functional theory (DFT) shows linear behavior with adjusted R2 values lying in the range of 0.80 to 0.94 for all the predicted properties for different types of Heusler alloys. Feature importance analysis shows that the valence electron numbers plays an important feature role in the prediction for most of the predicted outcomes. Case studies with one full Heusler alloy and one quaternary Heusler alloy were also mentioned comparing the machine learning predicted results with our earlier theoretical calculated values and experimentally measured results, suggesting high accuracy of the model predicted results.

We propose a Model Predictive Control (MPC) for collision avoidance between an autonomous agent and dynamic obstacles with uncertain predictions. The collision avoidance constraints are imposed by enforcing positive distance between convex sets representing the agent and the obstacles, and tractably reformulating them using Lagrange duality. This approach allows for smooth collision avoidance constraints even for polytopes, which otherwise require mixed-integer or non-smooth constraints. We consider three widely used descriptions of the uncertain obstacle position: 1) Arbitrary distribution with polytopic support, 2) Gaussian distributions and 3) Arbitrary distribution with first two moments known. For each case we obtain deterministic reformulations of the collision avoidance constraints. The proposed MPC formulation optimizes over feedback policies to reduce conservatism in satisfying the collision avoidance constraints. The proposed approach is validated using simulations of traffic intersections in CARLA.

As a typical application of deep learning, physics-informed neural network (PINN) {has been} successfully used to find numerical solutions of partial differential equations (PDEs), but how to improve the limited accuracy is still a great challenge for PINN. In this work, we introduce a new method, symmetry-enhanced physics informed neural network (SPINN) where the invariant surface conditions induced by the Lie symmetries or non-classical symmetries of PDEs are embedded into the loss function in PINN, to improve the accuracy of PINN for solving the forward and inverse problems of PDEs. We test the effectiveness of SPINN for the forward problem via two groups of ten independent numerical experiments using different numbers of collocation points and neurons for the heat equation, Korteweg-de Vries (KdV) equation and potential Burgers {equations} respectively, and for the inverse problem by considering different layers and neurons as well as different training points for the Burgers equation in potential form. The numerical results show that SPINN performs better than PINN with fewer training points and simpler architecture of neural network. Furthermore, we discuss the computational overhead of SPINN in terms of the relative computational cost to PINN and show that the training time of SPINN has no obvious increases, even less than PINN for certain cases.

Today marks exactly 10 years since NASA's Curiosity rover touched down on Mars. The one-tonne vehicle launched from Earth in November 2011 and – after an arduous nine-month journey which included the'seven minutes of terror' down to the Martian surface – it set out to look for evidence that the Red Planet may once have supported life. Since then, Curiosity has driven nearly 18 miles (29 kilometres) and ascended 2,050 feet (625 metres) as it explores Gale Crater and the foothills of Mount Sharp within it. The rover has analysed 41 rock and soil samples, relying on a suite of science instruments to learn what they reveal about Earth's rocky sibling. Such has been its success, what was originally intended to be a two-year mission was later extended indefinitely, leading to a rather busy decade.

How to do digital marketing in the age of privacy? Industry 4.0: Why thousands of small businesses continue to view digital transition with scepticism The timeline for public consultations on the new data bill may be announced by the end of August as the ministry of electronics and IT (MeitY) is at an "advanced stage with the draft of the comprehensive framework," a senior official told ET. The government withdrew the previous version of the bill on Wednesday. The Central Bureau of Investigation (CBI) sought day-to-day hearing of its appeals in the Delhi High Court against the acquittal of former telecom minister A Raja and other accused in the 2G spectrum case. India dropped its target of establishing 500 GW of renewable energy capacity by 2030, giving itself the flexibility of 50% power from non-fossil fuel sources by then in its commitments to the United Nations Framework Convention on Climate Change (UNFCCC).

For the task of identification of macroscopic variables from high-fidelity simulations/spatio-temporal data, various machine learning methods have been proposed including non-linear manifold learning algorithms such as Diffusion Maps (DMs) [5-13], ISOMAP [14-16] and Local Linear Embedding [17, 18] but also Autoencoders [19, 20]. For the task of the extraction of surrogate models for the approximation of the emergent dynamics, available approaches include the Sparse Identification of the Nonlinear Dynamics (SINDy) [21], the Koopman operator [22-27], Gaussian Processes [12, 18, 28], Artificial Neural Networks (ANNs) [12, 13], Recursive Neural Networks (RNN) [20], Deep Learning [29], as well as Long Short-Term Memory (LSTM) networks [30]. For the task of the bridging of the micro-and macro-scales, the Equation-free (EF) multiscale framework, introduced back in the early 2000s years, [1, 31, 32] bypasses the need to extract explicit, closed form macroscopic/surrogate models of any type (e.g.

Abstract-- Deep learning based latent representations have been widely used for numerous scientific visualization applications such as isosurface similarity analysis, volume rendering, flow field synthesis, and data reduction, just to name a few. However, existing latent representations are mostly generated from raw data in an unsupervised manner, which makes it difficult to incorporate domain interest to control the size of the latent representations and the quality of the reconstructed data. In this paper, we present a novel importance-driven latent representation to facilitate domain-interest-guided scientific data visualization and analysis. We utilize spatial importance maps to represent various scientific interests and take them as the input to a feature transformation network to guide latent generation. We further reduced the latent size by a lossless entropy encoding algorithm trained together with the autoencoder, improving the storage and memory efficiency. We qualitatively and quantitatively evaluate the effectiveness and efficiency of latent representations generated by our method with data from multiple scientific visualization applications. First, to incorporate domain by autoencoders have attracted great attentions of researchers in recent interests into latent representations, we extend the basic autoencoder years. Latent representations have been successfully demonstrated to with a feature transformation network that takes domain interest as an retain essential information in the original data, and can be used for input to guide the mapping from scientific data to latent representations. Every been applied to multivariate volumetric data [28], streamlines and element in the importance map is a real value indicating how vital this stream surfaces [18], isosurfaces [12], and particles [25]. The importance Although latent representations for large-scale scientific data have values can be derived mathematically based on the domain or been used extensively, there are still several challenges. First, domain heuristically based on distances, distributions, locations, etc., depending scientists have diverse interests in different data portions, but latent on the underlying scientific applications.

Machine learning plays an increasingly important role in many areas of chemistry and materials science, e.g. to predict materials properties, to accelerate simulations, to design new materials, and to predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this review article, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs.