Portfolio optimization emerged with the seminal paper of Markowitz (1952). The original mean-variance framework is appealing because it is very efficient from a computational point of view. However, it also has one well-established failing since it can lead to portfolios that are not optimal from a financial point of view. Nevertheless, very few models have succeeded in providing a real alternative solution to the Markowitz model. The main reason lies in the fact that most academic portfolio optimization models are intractable in real life although they present solid theoretical properties. By intractable we mean that they can be implemented for an investment universe with a small number of assets using a lot of computational resources and skills, but they are unable to manage a universe with dozens or hundreds of assets. However, the emergence and the rapid development of robo-advisors means that we need to rethink portfolio optimization and go beyond the traditional mean-variance optimization approach. Another industry has faced similar issues concerning large-scale optimization problems. Machine learning has long been associated with linear and logistic regression models. Again, the reason was the inability of optimization algorithms to solve high-dimensional industrial problems. Nevertheless, the end of the 1990s marked an important turning point with the development and the rediscovery of several methods that have since produced impressive results. The goal of this paper is to show how portfolio allocation can benefit from the development of these large-scale optimization algorithms. Not all of these algorithms are useful in our case, but four of them are essential when solving complex portfolio optimization problems. These four algorithms are the coordinate descent, the alternating direction method of multipliers, the proximal gradient method and the Dykstra's algorithm.

We derive a data-driven method for the approximation of the Koopman generator called gEDMD, which can be regarded as a straightforward extension of EDMD (extended dynamic mode decomposition). This approach is applicable to deterministic and stochastic dynamical systems. It can be used for computing eigenvalues, eigenfunctions, and modes of the generator and for system identification. In addition to learning the governing equations of deterministic systems, which then reduces to SINDy (sparse identification of nonlinear dynamics), it is possible to identify the drift and diffusion terms of stochastic differential equations from data. Moreover, we apply gEDMD to derive coarse-grained models of high-dimensional systems, and also to determine efficient model predictive control strategies. We highlight relationships with other methods and demonstrate the efficacy of the proposed methods using several guiding examples and prototypical molecular dynamics problems.

This paper presents a methodology and workflow that overcome the limitations of the conventional Generative Adversarial Networks (GANs) for geological facies modeling. It attempts to improve the training stability and guarantee the diversity of the generated geology through interpretable latent vectors. The resulting samples are ensured to have the equal probability (or an unbiased distribution) as from the training dataset. This is critical when applying GANs to generate unbiased and representative geological models that can be further used to facilitate objective uncertainty evaluation and optimal decision-making in oil field exploration and development. We proposed and implemented a new variant of GANs called Info-WGAN for the geological facies modeling that combines Information Maximizing Generative Adversarial Network (InfoGAN) with Wasserstein distance and Gradient Penalty (GP) for learning interpretable latent codes as well as generating stable and unbiased distribution from the training data. Different from the original GAN design, InfoGAN can use the training images with full, partial, or no labels to perform disentanglement of the complex sedimentary types exhibited in the training dataset to achieve the variety and diversity of the generated samples. This is accomplished by adding additional categorical variables that provide disentangled semantic representations besides the mere randomized latent vector used in the original GANs. By such means, a regularization term is used to maximize the mutual information between such latent categorical codes and the generated geological facies in the loss function. Furthermore, the resulting unbiased sampling by Info-WGAN makes the data conditioning much easier than the conventional GANs in geological modeling because of the variety and diversity as well as the equal probability of the unconditional sampling by the generator.

For dynamical systems that can be modelled as asymptotically stable linear systems forced by Gaussian noise, this paper develops methods to infer their modes from observations in real time. The modes can be real or complex. For a real mode, we infer its damping rate, mode shape and amplitude. For a complex mode, we infer its frequency, damping rate, (complex) mode shape and (complex) amplitude. The work is motivated and illustrated by the problem of detection of oscillations in power flow in AC electrical networks. Suggestions of other applications are given.

Palo Alto Research Center, Mail Stop: 3333 Coyote Hill Road, Palo Alto, CA 94034 USA Abstract Our research aims at developing intelligent systems to reduce the transportation-related energy expenditure of a large city by influencing individual behavior. We introduce Copter - an intelligent travel assistant that evaluates multi-modal travel alternatives to find a plan that is acceptable to a person given their context and preferences. We propose a formulation for acceptable planning that brings together ideas from AI, machine learning, and economics. This formulation has been incorporated in Copter that produces acceptable plans in real-time. We adopt a novel empirical evaluation framework that combines human decision data with a high fidelity multi-modal transportation simulation to demonstrate a 4% energy reduction and 20% delay reduction in a realistic deployment scenario in Los Angeles, California, USA. 1. Introduction Transportation is one of the largest consumers of energy in the ...

This paper proposes a formulation of the Active Debris Removal (ADR) Mission Design problem as a modified Time-Dependent Traveling Salesman Problem (TDTSP). The TDTSP is a well-known combinatorial optimization problem, whose solution is the cheapest mono-cyclic tour connecting a number of non-stationary cities in a map. The problem is tackled with an optimization procedure based on Simulated Annealing, that efficiently exploits a natural encoding and a careful choice of mutation operators. The developed algorithm is used to simultaneously optimize the targets sequence and the rendezvous epochs of an impulsive ADR mission. Numerical results are presented for sets comprising up to 20 targets. INTRODUCTION The Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem, whose solution is the cheapest tour which allows a salesman to visit, only once, a number of cities in a map; the cost of each city-to-city transfer is, typically, the traveled distance or the fuel consumption. Active Debris Removal (ADR) missions can be seen as peculiar instances of the TDTSP, where an active (chaser) spacecraft is asked to visit, that is, to perform a rendezvous, with a certain number of targets (space debris), making the best use of the on-board propellant. Such kind of missions are increasing in popularity among space agencies all over the world, as the sustainability of the extra-atmospheric environment is becoming compromised by the huge amount of "space garbage" now orbiting Earth. A cost-competitive space program would involve the removal of several dozens of small debris with each single mission; such a complex scenario could became feasible only with the best possible use of the propellant on-board of the chaser spacecraft. As a consequence, a well-designed ADR mission would require the optimization of a multi-target rendezvous trajectory. A number of authors dealt with long term or time-free ADR missions aimed at removing a small number of debris from Sun synchronous orbits (at a rate of three to ten per year). These missions heavily rely on J 2 orbital perturbation for the alignment of the orbital planes of consecutive targets before starting the rendezvous maneuver, in order to reduce the mission cost.

Noname manuscript No. (will be inserted by the editor)Active Collaboration in Relative Observation for Multi-agent Visual SLAM based on Deep Q Network Zhaoyi Pei · Piaosong Hao · Meixiang Quan · Muhammad Zuhair Qadir · Guo Li Received: date / Accepted: date Abstract This paper proposes a unique active relative localization mechanism for multi-agent Simultaneous Localization and Mapping(SLAM),in which a agent to be observed are considered as a task, which is performed by others assisting that agent by relative observation. A task allocation algorithm based on deep reinforcement learning are proposed for this mechanism. Each agent can choose whether to localize other agents or to continue independent SLAM on it own initiative. By this way, the process of each agent SLAM will be interacted by the collaboration. Firstly, based on the characteristics of ORBSLAM, a unique observation function which models the whole MAS is obtained. Secondly, a novel type of Deep Q network(DQN) called MAS-DQN is deployed to learn correspondence between Q Value and state-action pair, abstract representation of agents in MAS are learned in the process of collaboration amongZhaoyi Pei Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China Email: peizhaoyi@stu.hit.edu.cn Songhao Piao Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China Email: piaosh@hit.edu.cn Meixiang Quan Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China Email: 15b903042@hit.edu.cn

Already attracting considerable industry interest in GeoSoftware's PowerLog petrophysical software, Python ecosystems in HampsonRussell and Jason will let experts and data scientists completely customize machine learning and reservoir characterization workflows by using extensively available Python machine learning libraries and also their own proprietary code. Python ecosystems allow users to efficiently research and test various state-of-the-art machine learning workflows for proof-of-concept or commercial projects. G&G experts and data scientists can use Ecosystem workflows pre-built by CGG or they can build their own new reservoir characterization workflows using the latest open source machine learning packages, such as Google's TensorFlow. HampsonRussell and Jason users, even those with limited expertise in machine learning or Python scripting, will now benefit from complete control over input data and analysis output. With Python ecosystems, users can process data with pre-built or client-proprietary Python scripts or Jupyter notebooks, and store input and output data in either a HampsonRussell or Jason project database or a shared directory.

As society turns to artificial intelligence to solve problems across ever more domains, we're seeing an arms race to create specialized hardware that can run deep learning models at higher speeds and lower power consumption. Some recent breakthroughs in this race include new chip architectures that perform computations in ways that are fundamentally different from what we've seen before. Looking at their capabilities gives us an idea of the kinds of AI applications we could see emerging over the next couple of years. Neural networks, composed of thousands and millions of small programs that perform simple calculations to perform complicated tasks such as detecting objects in images or converting speech to text are key to deep learning. But traditional computers are not optimized for neural network operations.

This piece has been published as part of Slate's partnership with Covering Climate Now, a global collaboration of more than 250 news outlets to strengthen coverage of the climate story. Artificial intelligence is getting smarter, but it isn't getting cleaner. In order to improve at predicting the weather, sorting your social media feeds, and hailing your Uber, it needs to train on massive datasets. A few years ago, an A.I. system might have required millions of words to attempt to learn a language, but today that same system could be processing 40 billion words as it trains, according to Roy Schwartz, who researches deep learning models at the Allen Institute for Artificial Intelligence and in the University of Washington's computer science and engineering department. All that processing takes a lot of energy.