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Multiscale Computation and Dynamic Attention in Biological and Artificial
brain sciences Review Multiscale Computation and Dynamic Attention in Biological and Artificial Intelligence Ryan Paul Badman 1,* , Thomas Trenholm Hills 2 and Rei Akaishi 1,* 1 Center for Brain Science, RIKEN, Saitama 351-0198, Japan 2 Department of Psychology, University of Warwick, Coventry CV4 7AL, UK; [email protected] * Correspondence: [email protected] (R.P.B.); [email protected] (R.A.) Received: 4 March 2020; Accepted: 17 June 2020; Published: 20 June 2020 Abstract: Biological and artificial intelligence (AI) are often defined by their capacity to achieve a hierarchy of short-term and long-term goals that require incorporating information over time and space at both local and global scales. More advanced forms of this capacity involve the adaptive modulation of integration across scales, which resolve computational inefficiency and explore-exploit dilemmas at the same time. Research in neuroscience and AI have both made progress towards understanding architectures that achieve this. Insight into biological computations come from phenomena such as decision inertia, habit formation, information search, risky choices and foraging. Across these domains, the brain is equipped with mechanisms (such as the dorsal anterior cingulate and dorsolateral prefrontal cortex) that can represent and modulate across scales, both with top-down control processes and by local to global consolidation as information progresses from sensory to prefrontal areas. Paralleling these biological architectures, progress in AI is marked by innovations in dynamic multiscale modulation, moving from recurrent and convolutional neural networks—with fixed scalings—to attention, transformers, dynamic convolutions, and consciousness priors—which modulate scale to input and increase scale breadth. -
Deep Neural Network Models for Sequence Labeling and Coreference Tasks
Federal state autonomous educational institution for higher education ¾Moscow institute of physics and technology (national research university)¿ On the rights of a manuscript Le The Anh DEEP NEURAL NETWORK MODELS FOR SEQUENCE LABELING AND COREFERENCE TASKS Specialty 05.13.01 - ¾System analysis, control theory, and information processing (information and technical systems)¿ A dissertation submitted in requirements for the degree of candidate of technical sciences Supervisor: PhD of physical and mathematical sciences Burtsev Mikhail Sergeevich Dolgoprudny - 2020 Федеральное государственное автономное образовательное учреждение высшего образования ¾Московский физико-технический институт (национальный исследовательский университет)¿ На правах рукописи Ле Тхе Ань ГЛУБОКИЕ НЕЙРОСЕТЕВЫЕ МОДЕЛИ ДЛЯ ЗАДАЧ РАЗМЕТКИ ПОСЛЕДОВАТЕЛЬНОСТИ И РАЗРЕШЕНИЯ КОРЕФЕРЕНЦИИ Специальность 05.13.01 – ¾Системный анализ, управление и обработка информации (информационные и технические системы)¿ Диссертация на соискание учёной степени кандидата технических наук Научный руководитель: кандидат физико-математических наук Бурцев Михаил Сергеевич Долгопрудный - 2020 Contents Abstract 4 Acknowledgments 6 Abbreviations 7 List of Figures 11 List of Tables 13 1 Introduction 14 1.1 Overview of Deep Learning . 14 1.1.1 Artificial Intelligence, Machine Learning, and Deep Learning . 14 1.1.2 Milestones in Deep Learning History . 16 1.1.3 Types of Machine Learning Models . 16 1.2 Brief Overview of Natural Language Processing . 18 1.3 Dissertation Overview . 20 1.3.1 Scientific Actuality of the Research . 20 1.3.2 The Goal and Task of the Dissertation . 20 1.3.3 Scientific Novelty . 21 1.3.4 Theoretical and Practical Value of the Work in the Dissertation . 21 1.3.5 Statements to be Defended . 22 1.3.6 Presentations and Validation of the Research Results . -
Mscthesis Joseangelgutier ... Humada.Pdf
Targeting a Mars science orbit from Earth using Dual Chemical-Electric Propulsion and Ballistic Capture Jose Angel Gutierrez Ahumada Delft University of Technology TARGETING A MARS SCIENCE ORBIT FROM EARTH USING DUAL CHEMICAL-ELECTRIC PROPULSION AND BALLISTIC CAPTURE by Jose Angel Gutierrez Ahumada MSc Thesis in partial fulfillment of the requirements for the degree of Master of Science in Aerospace Engineering at the Delft University of Technology, to be defended publicly on Wednesday May 8, 2019. Supervisor: Dr. Francesco Topputo, TU Delft/Politecnico di Milano Dr. Ryan Russell, The University of Texas at Austin Thesis committee: Dr. Francesco Topputo, TU Delft/Politecnico di Milano Prof. dr. ir. Pieter N.A.M. Visser, TU Delft ir. Ron Noomen, TU Delft Dr. Angelo Cervone, TU Delft This thesis is confidential and cannot be made public until December 31, 2020. An electronic version of this thesis is available at http://repository.tudelft.nl/. Cover picture adapted from https://steemitimages.com/p/2gs...QbQvi EXECUTIVE SUMMARY Ballistic capture is a relatively novel concept in interplanetary mission design with the potential to make Mars and other targets in the Solar System more accessible. A complete end-to-end interplanetary mission from an Earth-bound orbit to a stable science orbit around Mars (in this case, an areostationary orbit) has been conducted using this concept. Sets of initial conditions leading to ballistic capture are generated for different epochs. The influence of the dynamical model on the capture is also explored briefly. Specific capture trajectories are then selected based on a study of their stabilization into an areostationary orbit. -
Convolutional Neural Network Model Layers Improvement for Segmentation and Classification on Kidney Stone Images Using Keras and Tensorflow
Journal of Multidisciplinary Engineering Science and Technology (JMEST) ISSN: 2458-9403 Vol. 8 Issue 6, June - 2021 Convolutional Neural Network Model Layers Improvement For Segmentation And Classification On Kidney Stone Images Using Keras And Tensorflow Orobosa Libert Joseph Waliu Olalekan Apena Department of Computer / Electrical and (1) Department of Computer / Electrical and Electronics Engineering, The Federal University of Electronics Engineering, The Federal University of Technology, Akure. Nigeria. Technology, Akure. Nigeria. [email protected] (2) Biomedical Computing and Engineering Technology, Applied Research Group, Coventry University, Coventry, United Kingdom [email protected], [email protected] Abstract—Convolutional neural network (CNN) and analysis, by automatic or semiautomatic means, of models are beneficial to image classification large quantities of data in order to discover meaningful algorithms training for highly abstract features patterns [1,2]. The domains of data mining include: and work with less parameter. Over-fitting, image mining, opinion mining, web mining, text mining, exploding gradient, and class imbalance are CNN and graph mining and so on. Some of its applications major challenges during training; with appropriate include anomaly detection, financial data analysis, management training, these issues can be medical data analysis, social network analysis, market diminished and enhance model performance. The analysis [3]. Recent progress in deep learning using models are 128 by 128 CNN-ML and 256 by 256 CNN machine learning (CNN-ML) has been helpful in CNN-ML training (or learning) and classification. decision support and contributed to positive outcome, The results were compared for each model significantly. The application of CNN-ML to diverse classifier. The study of 128 by 128 CNN-ML model areas of soft computing is adapted diagnosis has the following evaluation results consideration procedure to enhance time and accuracy [4]. -
Interplanetary Cruising
Interplanetary Cruising 1. Foreword The following material is intended to supplement NASA-JSC's High School Aerospace Scholars (HAS) instructional reading. The author, a retired veteran of 60 Space Shuttle flights at Mission Control's Flight Dynamics Officer (FDO) Console, first served as HAS mentor in association with a June 2008 workshop. On that occasion, he noticed students were generally unfamiliar with the nature and design of spacecraft trajectories facilitating travel between Earth and Mars. Much of the specific trajectory design data appearing herein was developed during that and subsequent workshops as reference material for students integrating it into Mars mission timeline, spacecraft mass, landing site selection, and cost estimates. Interplanetary Cruising is therefore submitted to HAS for student use both before and during Mars or other interplanetary mission planning workshops. Notation in this document distinguishes vectors from scalars using bold characters in a vector's variable name. The "•" operator denotes a scalar product between two vectors. If vectors a and b are represented with scalar coordinates such that a = (a1, a2, a3) and b = (b1, b2, b3) in some 3- dimensional Cartesian system, a • b = a1 b1 + a2 b2 + a3 b3 = a b cos γ, where γ is the angle between a and b. 2. Introduction Material in this reference will help us understand the pedigree and limitations applying to paths (trajectories) followed by spacecraft about the Sun as they "cruise" through interplanetary space. Although cruise between Earth and Mars is used in specific examples, this lesson's physics would apply to travel between any two reasonably isolated objects in our solar system. -
The History Began from Alexnet: a Comprehensive Survey on Deep Learning Approaches
> REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 1 The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches Md Zahangir Alom1, Tarek M. Taha1, Chris Yakopcic1, Stefan Westberg1, Paheding Sidike2, Mst Shamima Nasrin1, Brian C Van Essen3, Abdul A S. Awwal3, and Vijayan K. Asari1 Abstract—In recent years, deep learning has garnered I. INTRODUCTION tremendous success in a variety of application domains. This new ince the 1950s, a small subset of Artificial Intelligence (AI), field of machine learning has been growing rapidly, and has been applied to most traditional application domains, as well as some S often called Machine Learning (ML), has revolutionized new areas that present more opportunities. Different methods several fields in the last few decades. Neural Networks have been proposed based on different categories of learning, (NN) are a subfield of ML, and it was this subfield that spawned including supervised, semi-supervised, and un-supervised Deep Learning (DL). Since its inception DL has been creating learning. Experimental results show state-of-the-art performance ever larger disruptions, showing outstanding success in almost using deep learning when compared to traditional machine every application domain. Fig. 1 shows, the taxonomy of AI. learning approaches in the fields of image processing, computer DL (using either deep architecture of learning or hierarchical vision, speech recognition, machine translation, art, medical learning approaches) is a class of ML developed largely from imaging, medical information processing, robotics and control, 2006 onward. Learning is a procedure consisting of estimating bio-informatics, natural language processing (NLP), cybersecurity, and many others. -
The Understanding of Convolutional Neuron Network Family
2017 2nd International Conference on Computer Engineering, Information Science and Internet Technology (CII 2017) ISBN: 978-1-60595-504-9 The Understanding of Convolutional Neuron Network Family XINGQUN QI ABSTRACT Along with the development of the computing speed and Artificial Intelligence, more and more jobs have been done by computers. Convolutional Neural Network (CNN) is one of the most popular algorithms in the field of Deep Learning. It is mainly used in computer identification, especially in voice, text recognition and other aspects of the application. CNNs have been developed for decades. In theory, Convolutional Neural Network has gradually become more mature. However, in practical applications, it is limited by various conditions. This paper will introduce the course of Convolutional Neural Network’s development today, as well as the current more mature and popular architecture and related applications of it, to help readers have more macro and comprehensive understanding of Convolutional Neural Networks. KEYWORDS Convolutional Neuron Network, Architecture, CNN family, Application INTRODUCTION With the continuous improvements of technology, there are more and more multi- disciplinary applications achieved with computer technology. And Machine Learning, especially Deep Learning is one of the most indispensable and important parts of that technology. Deep learning has the great advantage of acquiring features and modeling from data [1]. The Convolutional Neural Network (CNN) algorithm is one of the best and foremost algorithms in the Deep Learning algorithms. It is mainly used for image recognition and computer vision. In today’s living conditions, the impact and significance of CNN is becoming more and more important. In computer vision, CNN has an important position. -
Methods and Trends in Natural Language Processing Applications in Big Data
International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-7, Issue-6S5, April 2019 Methods and Trends in Natural Language Processing Applications in Big Data Joseph M. De Guia, Madhavi Devaraj translation that can be processed and accessible to computer Abstract: Understanding the natural language of humans by applications. Some of the tasks available to NLP can be the processing them in the computer makes sense for applications in following: machine translation, generation and understanding solving practical problems. These applications are present in our of natural language, morphological separation, part of speech systems that automates some and even most of the human tasks performed by the computer algorithms.The “big data” deals with tagging, recognition of speech, entities, optical characters, NLP techniques and applications that sifts through each word, analysis of discourse, sentiment analysis, etc. Through these phrase, sentence, paragraphs, symbols, images, speech, tasks, NLPs achieved the goal of analyzing, understanding, utterances with its meanings, relationships, and translations that and generating the natural language of human using a can be processed and accessible to computer applications by computer application and with the help of learning humans. Through these automated tasks, NLPs achieved the goal techniques. of analyzing, understanding, and generating the natural This paper is a survey of the published literature in NLP and language of human using a computer application and with the its uses to big data. This also presents a review of the NLP help of classic and advanced machine learning techniques. This paper is a survey of the published literature in NLP and its uses to applications and learning techniques used in some of the big data. -
A Delta-V Map of the Known Main Belt Asteroids
Acta Astronautica 146 (2018) 73–82 Contents lists available at ScienceDirect Acta Astronautica journal homepage: www.elsevier.com/locate/actaastro A Delta-V map of the known Main Belt Asteroids Anthony Taylor *, Jonathan C. McDowell, Martin Elvis Harvard-Smithsonian Center for Astrophysics, 60 Garden St., Cambridge, MA, USA ARTICLE INFO ABSTRACT Keywords: With the lowered costs of rocket technology and the commercialization of the space industry, asteroid mining is Asteroid mining becoming both feasible and potentially profitable. Although the first targets for mining will be the most accessible Main belt near Earth objects (NEOs), the Main Belt contains 106 times more material by mass. The large scale expansion of NEO this new asteroid mining industry is contingent on being able to rendezvous with Main Belt asteroids (MBAs), and Delta-v so on the velocity change required of mining spacecraft (delta-v). This paper develops two different flight burn Shoemaker-Helin schemes, both starting from Low Earth Orbit (LEO) and ending with a successful MBA rendezvous. These methods are then applied to the 700,000 asteroids in the Minor Planet Center (MPC) database with well-determined orbits to find low delta-v mining targets among the MBAs. There are 3986 potential MBA targets with a delta- v < 8kmsÀ1, but the distribution is steep and reduces to just 4 with delta-v < 7kms-1. The two burn methods are compared and the orbital parameters of low delta-v MBAs are explored. 1. Introduction [2]. A running tabulation of the accessibility of NEOs is maintained on- line by Lance Benner3 and identifies 65 NEOs with delta-v< 4:5kms-1.A For decades, asteroid mining and exploration has been largely dis- separate database based on intensive numerical trajectory calculations is missed as infeasible and unprofitable. -
Orbital Mechanics of Gravitational Slingshots 1 Introduction 2 Approach
Orbital Mechanics of Gravitational Slingshots Final Paper 15-424: Foundations of Cyber-Physical Systems Adam Moran, [email protected] John Mann, [email protected] May 1, 2016 Abstract A gravitational slingshot is a maneuver to save fuel by using the gravity of a planet to accelerate or decelerate a spacecraft. Due to the large distances and high speeds involved, slingshots require precise accuracy to accomplish | the slightest mistake could cause the whole mission to fail. Therefore, we have developed a cyber-physical system to model the physics and prove the safety and efficiency of powered and unpowered gravitational slingshots. We present our findings and proof in this paper. 1 Introduction A gravitational slingshot is a maneuver performed to increase or decrease the speed of a spacecraft by simply approaching planetary bodies. A spacecraft's usefulness and maneuverability is basically tied to the amount of fuel it can carry, and the more fuel a spacecraft holds, the more fuel it needs to carry that fuel into orbit. Therefore, gravitational slingshots are a very appealing way to save mass, and therefore money, on deep-space missions since these maneuvers do not require any fuel. As missions conducted by national and private space programs become more frequent and ambitious, the need for these precise maneuvers will increase. Therefore, we have created a cyber-physical system that models the physics of a gravitational slingshot for a spacecraft approaching a planet. In the "Approach" section of this paper, we give a brief overview of the physics involved with orbits and gravitational slingshots. In the "Models and Properties" section of this paper, we describe what assumptions and simplifications we made to model these astrophysics in a way for us to prove our desired properties with KeYmaeraX. -
The Neocognitron As a System for Handavritten Character Recognition: Limitations and Improvements
The Neocognitron as a System for HandAvritten Character Recognition: Limitations and Improvements David R. Lovell A thesis submitted for the degree of Doctor of Philosophy Department of Electrical and Computer Engineering University of Queensland March 14, 1994 THEUliW^^ This document was prepared using T^X and WT^^. Figures were prepared using tgif which is copyright © 1992 William Chia-Wei Cheng (william(Dcs .UCLA. edu). Graphs were produced with gnuplot which is copyright © 1991 Thomas Williams and Colin Kelley. T^ is a trademark of the American Mathematical Society. Statement of Originality The work presented in this thesis is, to the best of my knowledge and belief, original, except as acknowledged in the text, and the material has not been subnaitted, either in whole or in part, for a degree at this or any other university. David R. Lovell, March 14, 1994 Abstract This thesis is about the neocognitron, a neural network that was proposed by Fuku- shima in 1979. Inspired by Hubel and Wiesel's serial model of processing in the visual cortex, the neocognitron was initially intended as a self-organizing model of vision, however, we are concerned with the supervised version of the network, put forward by Fukushima in 1983. Through "training with a teacher", Fukushima hoped to obtain a character recognition system that was tolerant of shifts and deformations in input images. Until now though, it has not been clear whether Fukushima's ap- proach has resulted in a network that can rival the performance of other recognition systems. In the first three chapters of this thesis, the biological basis, operational principles and mathematical implementation of the supervised neocognitron are presented in detail. -
Memory Based Machine Intelligence Techniques in VLSI Hardware
Memory Based Machine Intelligence Techniques in VLSI hardware Alex Pappachen James, Machine Intelligence Research Group, Indian Institute of Information Technology and Management-Kerala Email: [email protected] Abstract: We briefly introduce the memory based approaches to emulate machine intelligence in VLSI hardware, describing the challenges and advantages. Implementation of artificial intelligence techniques in VLSI hardware is a practical and difficult problem. Deep architectures, hierarchical temporal memories and memory networks are some of the contemporary approaches in this area of research. The techniques attempt to emulate low level intelligence tasks and aim at providing scalable solutions to high level intelligence problems such as sparse coding and contextual processing. Neocortex in human brain that accounts for 76% of the brain's volume can be seen as a control unit that is involved in the processing of intelligence functions such as sensory perception, generation of motor commands, spatial reasoning, conscious thought and language. The realisation of neocortex like functions through algorithms and hardware implementation has been the inspiration to majority of state of the art artificial intelligence systems and theory. Intelligent functions are often required to be performed in environments having high levels of natural variability, which makes individual functions of the brain highly complex. On the other hand, the huge number of neurons in neocortex and their interconnections with each other makes the brain structurally very complex. As a result, the mimicking of neocortex is exceptionally challenging and difficult. Human brain is highly modular and hierarchical in structure and functionality. Each module in a human brain has evolved in structure and is trained to deal with very specific task.