This Hedge Fund Stocks Package is designed for investors and analysts who need predictions of the best-performing stocks according to I Know First's stock market algorithm. Package Name: Hedge Fund Stocks Recommended Positions: Long Forecast Length: 1 Year (12/06/2017 – 12/06/2018) I Know First Average: 23.31% Several predictions in this long-term 1 Year forecast saw significant returns. The algorithm had correctly predicted 6 out 10 stock movements. UAA was the top performing prediction with a return of 85.01%. With these notable trade returns, the package itself registered an average return of 23.31% compared to the S&P 500's return of 27.52% for the same period.

Methods proposed in the literature towards continual deep learning typically operate in a task-based sequential learning setup. A sequence of tasks is learned, one at a time, with all data of current task available but not of previous or future tasks. Task boundaries and identities are known at all times. This setup, however, is rarely encountered in practical applications. Therefore we investigate how to transform continual learning to an online setup. We develop a system that keeps on learning over time in a streaming fashion, with data distributions gradually changing and without the notion of separate tasks. To this end, we build on the work on Memory Aware Synapses, and show how this method can be made online by providing a protocol to decide i) when to update the importance weights, ii) which data to use to update them, and iii) how to accumulate the importance weights at each update step. Experimental results show the validity of the approach in the context of two applications: (self-)supervised learning of a face recognition model by watching soap series and learning a robot to avoid collisions.

Abstract-- In an automated search system, similarity is a key concept in solving a human task. Indeed, human process is usually a natural categorization that underlies many natural abilities such as image recovery, language comprehension, decision making, or pattern recognition. In the image search axis, there are several ways to measure the similarity between images in an image database, to a query image. Image search by content is based on the similarity of the visual characteristics of the images. The distance function used to evaluate the similarity between images depends on the criteria of the search but also on the representation of the characteristics of the image; this is the main idea of the near and fuzzy sets approaches. In this article, we introduce a new category of beta type-2 fuzzy sets for the description of image characteristics as well as the near sets approach for image recovery. Finally, we illustrate our work with examples of image recovery problems used in the real world. I. INTRODUCTION He number of daily-generated images by websites and personal archives are constantly growing. Indeed, the effective management of the rapid expansion of visual information has become a major problem and a necessity for strengthening visual search technique based on visual content [3]. This necessity is behind the emergence of new visual search techniques based on visual content. It has been widely identified that the most efficient and intuitive way to research visual information is based on the properties that are extracted from the images themselves. Researchers from different communities ("Computer Vision" [4], "Database Management", "Man-machine Interface", "Information Retrieval") were attracted by this field. Since then, the search for images by content has developed quite rapidly. The intuitive idea of "any system that analyzes or automatically organizes a set of data or knowledge must use, in one form or another, a similarity operator whose purpose is to establish similarities or the relationships that exist between the manipulated information".

We present a pedagogical introduction to the recent advances in the computational geometry, physical implications, and data science of Calabi-Yau manifolds. Aimed at the beginning research student and using Calabi-Yau spaces as an exciting play-ground, we intend to teach some mathematics to the budding physicist, some physics to the budding mathematician, and some machine-learning to both. Based on various lecture series, colloquia and seminars given by the author in the past year, this writing is a very preliminary draft of a book to appear with Springer, by whose kind permission we post to ArXiv for comments and suggestions.

By 2050, 68 percent of the total global population will live in cities, according to the United Nations. By then, the world population will be 9.7 billion and 11.2 billion by 2100. The updated report from the United Nations states that currently, 55 percent of the world's population lives in urban areas. That means around 2.5 billion more people will be living in cities by 2050. India, China, and Nigeria combined will represent 35 percent of the projected urban population growth between 2018 and 2050.

Urban Traffic Networks are characterized by high dynamics of traffic flow and increased travel time, including waiting times. This leads to more complex road traffic management. The present research paper suggests an innovative advanced traffic management system based on Hierarchical Interval Type-2 Fuzzy Logic model optimized by the Particle Swarm Optimization (PSO) method. The aim of designing this system is to perform dynamic route assignment to relieve traffic congestion and limit the unexpected fluctuation effects on traffic flow. The suggested system is executed and simulated using SUMO, a well-known microscopic traffic simulator. For the present study, we have tested four large and heterogeneous metropolitan areas located in the cities of Sfax, Luxembourg, Bologna and Cologne. The experimental results proved the effectiveness of learning the Hierarchical Interval type-2 Fuzzy logic using real time particle swarm optimization technique PSO to accomplish multiobjective optimality regarding two criteria: number of vehicles that reach their destination and average travel time. The obtained results are encouraging, confirming the efficiency of the proposed system.

When we think about endangered animals in Afrika at risk of extinction or being poached, we usually think of elephants and rhinos. This can be attributed to various factors including increased publicity around the increasing threats that rhinos and elephants face from poachers. However, there are other endangered animal species in Afrika that also require as much protection and publicity. Take the addax antelopes in Niger as an example. In 2016, the Sahara Conservation Fund (SCF) released their research report which stated there were likely only a handful of addax antelopes, specifically only 3, remaining in the wild in Niger.

An estimated 4 billion people in the world lack a physical address. Without one, residents lose access to important services like package deliveries, medical care, and disaster relief, as well as the ability to register to vote or obtain a driver's license. Cities also have trouble planning new infrastructure, such as schools, water pipes, and electricity lines. "As you move into a more global economy and more people order and get goods delivered at a distance, you need a more specific address than'the house with the red door across from the cathedral,'" says Merry Law, the president of a company that provides international addressing information. Researchers at the MIT Media Lab and Facebook are now proposing a new way to address the unaddressed: with machine learning.

The economic and social impact of poor air quality in towns and cities is increasingly being recognised, together with the need for effective ways of creating awareness of real-time air quality levels and their impact on human health. With local authority maintained monitoring stations being geographically sparse and the resultant datasets also featuring missing labels, computational data-driven mechanisms are needed to address the data sparsity challenge. In this paper, we propose a machine learning-based method to accurately predict the Air Quality Index (AQI), using environmental monitoring data together with meteorological measurements. To do so, we develop an air quality estimation framework that implements a neural network that is enhanced with a novel Non-linear Autoregressive neural network with exogenous input (NARX), especially designed for time series prediction. The framework is applied to a case study featuring different monitoring sites in London, with comparisons against other standard machine-learning based predictive algorithms showing the feasibility and robust performance of the proposed method for different kinds of areas within an urban region.

A world built from accessible assets that drive human convenience and interaction. This is the future that's powered by the Internet of Things (IoT) and artificial intelligence (AI), two of the planet's hottest topic trends right now for a very good reason. They are also the fuel driving digital transformation in 2019. These are the technologies revolutionising performance, process and productivity. They are also transforming industry challenges across agriculture, retail, health and the public sector and are set to continue on this path well into 2019.