Inertial confinement fusion (ICF) experiments are designed using computer simulations that are approximations of reality, and therefore must be calibrated to accurately predict experimental observations. In this work, we propose a novel nonlinear technique for calibrating from simulations to experiments, or from low fidelity simulations to high fidelity simulations, via "transfer learning". Transfer learning is a commonly used technique in the machine learning community, in which models trained on one task are partially retrained to solve a separate, but related task, for which there is a limited quantity of data. We introduce the idea of hierarchical transfer learning, in which neural networks trained on low fidelity models are calibrated to high fidelity models, then to experimental data. This technique essentially bootstraps the calibration process, enabling the creation of models which predict high fidelity simulations or experiments with minimal computational cost. We apply this technique to a database of ICF simulations and experiments carried out at the Omega laser facility. Transfer learning with deep neural networks enables the creation of models that are more predictive of Omega experiments than simulations alone. The calibrated models accurately predict future Omega experiments, and are used to search for new, optimal implosion designs.

Discovering community structure in complex networks is a mature field since a tremendous number of community detection methods have been introduced in the literature. Nevertheless, it is still very challenging for practioners to determine which method would be suitable to get insights into the structural information of the networks they study. Many recent efforts have been devoted to investigating various quality scores of the community structure, but the problem of distinguishing between different types of communities is still open. In this paper, we propose a comparative, extensive and empirical study to investigate what types of communities many state-of-the-art and well-known community detection methods are producing. Specifically, we provide comprehensive analyses on computation time, community size distribution, a comparative evaluation of methods according to their optimisation schemes as well as a comparison of their partioning strategy through validation metrics. We process our analyses on a very large corpus of hundreds of networks from five different network categories and propose ways to classify community detection methods, helping a potential user to navigate the complex landscape of community detection.

Achieving high catalytic performance with the lowest possible amount of platinum is critical for fuel cell cost reduction. Here we describe a method of preparing highly active yet stable electrocatalysts containing ultralow-loading platinum content by using cobalt or bimetallic cobalt and zinc zeolitic imidazolate frameworks as precursors. Synergistic catalysis between strained platinum-cobalt core-shell nanoparticles over a platinum-group metal (PGM)–free catalytic substrate led to excellent fuel cell performance under 1 atmosphere of O2 or air at both high-voltage and high-current domains. Two catalysts achieved oxygen reduction reaction (ORR) mass activities of 1.08 amperes per milligram of platinum (A mgPt 1) and 1.77 A mgPt 1 and retained 64% and 15% of initial values after 30,000 voltage cycles in a fuel cell.

Vigilante citizens in a town in Arizona have slashed tyres, thrown rocks and even pointed guns at self-driving vehicles being tested in their neighbourhood, an investigation has revealed. Police in Chandler recorded 21 incidents over the past two years in which the autonomous vehicles and their test drivers were targeted by local residents. One incident on 24 October saw a man emerge from a park and slash the tyres of a Waymo vehicle stopped at an intersection. Earlier this year a Waymo test driver reported a man in shorts aiming a gun at his car when it passed the man's driveway. Police reports also show that rocks were thrown at Waymo's fleet on at least four separate occasions, according to The Arizona Republic, while other incidents include people yelling at the vehicles, chasing them and forcing them off the road.

Consider the problem of sequentially activating one of a finite number of independent bandits, where each activation of a bandit incurs a number of bandit dependent resource utilizations, or activation costs. For each resource type the constraint insures that the total resource utilized (or equivalently cost incurred) at any time does not exceed the current resource availability (budget). It is assumed that following each activation any unused resource amounts can be carried forward for use in future activations. We also make the assumption that successive activations of each bandit yield independent, among different bandits, identically distributed (iid) random rewards with positive means, and distributions that depend on unknown parameters. The objective isto obtain a feasible policy that maximizes asymptotically the total expect rewards or equivalently, minimizes asymptotically a regret function. We develop a class of feasible policies that are shown to be asymptotically optimal within a large class of good policies that uniformly fast (UF) convergent, in the sense of Burnetas and Katehakis (1996) and Lai and Robbins (1985). The results in this paper extend the work in Burnetas et al. (2017) which solved the case where there exists only one type of constraint for all bandits.

In the world today computer networks have a very important position and most of the urban and national infrastructure as well as organizations are managed by computer networks, therefore, the security of these systems against the planned attacks is of great importance. Therefore, researchers have been trying to find these vulnerabilities so that after identifying ways to penetrate the system, they will provide system protection through preventive or countermeasures. SVM is one of the major algorithms for intrusion detection. In this research, we studied a variety of malware and methods of intrusion detection, provide an efficient method for detecting attacks and utilizing dimension reduction.Thus, we will be able to detect attacks by carefully combining these two algorithms and pre-processes that are performed before the two on the input data. The main question raised is how we can identify attacks on computer networks with the above-mentioned method. In anomalies diagnostic method, by identifying behavior as a normal behavior for the user, the host, or the whole system, any deviation from this behavior is considered as an abnormal behavior, which can be a potential occurrence of an attack. The network intrusion detection system is used by anomaly detection method that uses the SVM algorithm for classification and SVD to reduce the size. Steps of the proposed method include pre-processing of the data set, feature selection, support vector machine, and evaluation.The NSL-KDD data set has been used to teach and test the proposed model. In this study, we inferred the intrusion detection using the SVM algorithm for classification and SVD for diminishing dimensions with no classification algorithm.Also the KNN algorithm has been compared in situations with and without diminishing dimensions,the results have shown that the proposed method has a better performance than comparable methods.

The objective of Reinforcement Learning is to learn an optimal policy by performing actions and observing their long term consequences. Unfortunately, acquiring such a policy can be a hard task. More severely, since one cannot tell if a policy is optimal, there is a constant need for exploration. This is known as the Exploration-Exploitation trade-off. In practice, this trade-off is resolved by using some inherent exploration mechanism, such as the $\epsilon$-greedy exploration, while still trying to learn the optimal policy. In this work, we take a different approach. We define a surrogate optimality objective: an optimal policy with respect to the exploration scheme. As we show throughout the paper, although solving this criterion does not necessarily lead to an optimal policy, the problem becomes easier to solve. We continue by analyzing this notion of optimality, devise algorithms derived from this approach, which reveal connections to existing work, and test them empirically on tabular and deep Reinforcement Learning domains.

Scheduling the maintenances of nuclear power plants is a complex optimization problem, formulated in 2-stage stochastic programming for the EURO/ROADEF 2010 challenge. The first level optimizes the maintenance dates and refueling decisions. The second level optimizes the production to fulfill the power demands and to ensure feasibility and costs of the first stage decisions. This paper solves a deterministic version of the problem, studying Mixed Integer Programming (MIP) formulations and matheuristics. Relaxing only two sets of constraints of the ROADEF challenge, a MIP formulation can be written using only binary variables for the maintenance dates. The MIP formulations are used to design constructive matheuristics and a Variable Neighborhood Descent (VND) local search. These matheuristics produce very high quality solutions. Some intermediate results explains results of the Challenge: the relaxation of constraints CT6 are justified and neighborhood analyses with MIP-VND justifies the choice of neighborhoods to implement for the problem. Lastly, an extension with stability costs for monthly reoptimization is considered, with efficient bi-objective matheuristics.

This paper presents several numerical applications of deep learning-based algorithms that have been analyzed in [11]. Numerical and comparative tests using TensorFlow illustrate the performance of our different algorithms, namely control learning by performance iteration (algorithms NNcontPI and ClassifPI), control learning by hybrid iteration (algorithms Hybrid-Now and Hybrid-LaterQ), on the 100-dimensional nonlinear PDEs examples from [6] and on quadratic Backward Stochastic Differential equations as in [5]. We also provide numerical results for an option hedging problem in finance, and energy storage problems arising in the valuation of gas storage and in microgrid management.

The robot will go through an initial assessment phase to check on its overall health and the health of its instruments before it can move on to the deployment phase. Then, once its finally time to deploy its suite of instruments, that process alone is expected to take two to three months. InSight will place its seismometer, and only once the team is happy with its location and initial operations will it return to the deck to get its wind and thermal shields, which will sit atop the seismometer for protection. The lander will then pick up the heat probe to bring to the surface, before beginning its historic dig. Eventually, once it's all settled in, Barrett says we'll be'sitting back listening for Mars quakes.'