The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time. Very few previous studies have examined this crucial and challenging weather forecasting problem from the machine learning perspective. In this paper, we formulate precipitation nowcasting as a spatiotemporal sequence forecasting problem in which both the input and the prediction target are spatiotemporal sequences. By extending the fully connected LSTM (FC-LSTM) to have convolutional structures in both the input-to-state and state-to-state transitions, we propose the convolutional LSTM (ConvLSTM) and use it to build an end-to-end trainable model for the precipitation nowcasting problem. Experiments show that our ConvLSTM network captures spatiotemporal correlations better and consistently outperforms FC-LSTM and the state-of-the-art operational ROVER algorithm for precipitation nowcasting.

Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

In Distributed Algorithms, Nancy Lynch provides a blueprint for designing, implementing, and analyzing distributed algorithms. She directs her book at a wide audience, including students, programmers, system designers, and researchers.



Distributed Algorithms contains the most significant algorithms and impossibility results in the area, all in a simple automata-theoretic setting. The algorithms are proved correct, and their complexity is analyzed according to precisely defined complexity measures. The problems covered include resource allocation, communication, consensus among distributed processes, data consistency, deadlock detection, leader election, global snapshots, and many others.



The material is organized according to the system model-first by the timing model and then by the interprocess communication mechanism. The material on system models is isolated in separate chapters for easy reference.



The presentation is completely rigorous, yet is intuitive enough for immediate comprehension. This book familiarizes readers with important problems, algorithms, and impossibility results in the area: readers can then recognize the problems when they arise in practice, apply the algorithms to solve them, and use the impossibility results to determine whether problems are unsolvable. The book also provides readers with the basic mathematical tools for designing new algorithms and proving new impossibility results. In addition, it teaches readers how to reason carefully about distributed algorithms-to model them formally, devise precise specifications for their required behavior, prove their correctness, and evaluate their performance with realistic measures.


Table of Contents

1 Introduction 
2 Modelling I; Synchronous Network Model 
3 Leader Election in a Synchronous Ring 
4 Algorithms in General Synchronous Networks 
5 Distributed Consensus with Link Failures 
6 Distributed Consensus with Process Failures 
7 More Consensus Problems 
8 Modelling II: Asynchronous System Model 
9 Modelling III: Asynchronous Shared Memory Model 
10 Mutual Exclusion 
11 Resource Allocation 
12 Consensus 
13 Atomic Objects 
14 Modelling IV: Asynchronous Network Model 
15 Basic Asynchronous Network Algorithms 
16 Synchronizers 
17 Shared Memory versus Networks 
18 Logical Time 
19 Global Snapshots and Stable Properties 
20 Network Resource Allocation 
21 Asynchronous Networks with Process Failures 
22 Data Link Protocols 
23 Partially Synchronous System Models 
24 Mutual Exclusion with Partial Synchrony 
25 Consensus with Partial Synchrony

Distributed algorithms

/pdf/convolutional-lstm-network-a-machine-learning-approach-for-3mrj9pedfq.pdf

Convolutional LSTM Network: a machine learning approach for precipitation nowcasting

Multiagent systems are made up of multiple interacting intelligent agents -- computational entities to some degree autonomous and able to cooperate, compete, communicate, act flexibly, and exercise control over their behavior within the frame of their objectives They are the enabling technology for a wide range of advanced applications relying on distributed and parallel processing of data, information, and knowledge relevant in domains ranging from industrial manufacturing to e-commerce to health care This book offers a state-of-the-art introduction to multiagent systems, covering the field in both breadth and depth, and treating both theory and practice It is suitable for classroom use or independent study This second edition has been completely revised, capturing the tremendous developments in multiagent systems since the first edition appeared in 1999 Sixteen of the book's seventeen chapters were written for this edition; all chapters are by leaders in the field, with each author contributing to the broad base of knowledge and experience on which the book rests The book covers basic concepts of computational agency from the perspective of both individual agents and agent organizations; communication among agents; coordination among agents; distributed cognition; development and engineering of multiagent systems; and background knowledge in logics and game theory Each chapter includes references, many illustrations and examples, and exercises of varying degrees of difficulty The chapters and the overall book are designed to be self-contained and understandable without additional material Supplemental resources are available on the book's Web site Contributors:Rafael Bordini, Felix Brandt, Amit Chopra, Vincent Conitzer, Virginia Dignum, Jurgen Dix, Ed Durfee, Edith Elkind, Ulle Endriss, Alessandro Farinelli, Shaheen Fatima, Michael Fisher, Nicholas R Jennings, Kevin Leyton-Brown, Evangelos Markakis, Lin Padgham, Julian Padget, Iyad Rahwan, Talal Rahwan, Alex Rogers, Jordi Sabater-Mir, Yoav Shoham, Munindar P Singh, Kagan Tumer, Karl Tuyls, Wiebe van der Hoek, Laurent Vercouter, Meritxell Vinyals, Michael Winikoff, Michael Wooldridge, Shlomo Zilberstein

Multiagent Systems

From the Publisher:
Among the many approaches to formal reasoning about programs, Dynamic Logic enjoys the singular advantage of being strongly related to classical logic. Its variants constitute natural generalizations and extensions of classical formalisms. For example, Propositional Dynamic Logic (PDL) can be described as a blend of three complementary classical ingredients: propositional calculus, modal logic, and the algebra of regular events. In First-Order Dynamic Logic (DL), the propositional calculus is replaced by classical first-order predicate calculus. Dynamic Logic is a system of remarkable unity that is theoretically rich as well as of practical value. It can be used for formalizing correctness specifications and proving rigorously that those specifications are met by a particular program. Other uses include determining the equivalence of programs, comparing the expressive power of various programming constructs, and synthesizing programs from specifications. 
This book provides the first comprehensive introduction to Dynamic Logic. It is divided into three parts. The first part reviews the appropriate fundamental concepts of logic and computability theory and can stand alone as an introduction to these topics. The second part discusses PDL and its variants, and the third part discusses DL and its variants. Examples are provided throughout, and exercises and a short historical section are included at the end of each chapter.

Dynamic Logic

This paper introduces a general formulation of atomic snapshot memory, a shared memory partitioned into words written (updated) by individual processes, or instantaneously read (scanned) in its entirety. This paper presents three wait-free implementations of atomic snapshot memory. The first implementation in this paper uses unbounded (integer) fields in these registers, and is particularly easy to understand. The second implementation uses bounded registers. Its correctness proof follows the ideas of the unbounded implementation. Both constructions implement a single-writer snapshot memory, in which each word may be updated by only one process, from single-writer, n-reader registers. The third algorithm implements a multi-writer snapshot memory from atomic n-writer, n-reader registers, again echoing key ideas from the earlier constructions. All operations require Θ(n2) reads and writes to the component shared registers in the worst case. —Authors' Abstract

Atomic snapshots of shared memory

This paper introduces a general formulation of atonuc wzap~hot rnenzory, a shared memory partitioned into words written (apduted) by individual processes, or instantaneously read (scanned) in its entirety. Thk paw’ Presents three wait-free implementations of atomic snapshot A preliminary version of this paper appeared in Proceedings of the 9th Annaa[ ACM SVmpmnwn on Plznctptes of’ Distributed Compafing (Quebec city. Quebec, A%). ACM New York, 199Q pp. 1-14. H. Attiya’s and N. Shavit’s research was partially supported by National Science Foundation grant CCR-86-1 1442, by Office of Naval Research contract NW014-S5-K-0168, and by DARPA cmltracts NOO014-83-K-0125 and NOO014-89-J1988. E. Gafni’s research was partially supported by National Science Foundation Grant DCR 84-51396 and XEROX Co. grant W8S1111. Part of this work was done while N. Shavit was at Hebrew University, Jerusalem, visiting AT&T Bell Laboratories and the Theory of Distributed Systems Group at Massachusetts Institute of Technology, and while H. Attiya was at the LaboratoV for Computer Science at Massachusetts Institute of Technology. Authors’ present addresses: Y. Afek, Computer Science Department. Tel-Aviv University, Ramat-Aviv, Israel 69978; H. Attiya, Department of Computer Science, Technion, Haifa, Israel 3~000:” D Dolev, Department of computer Science, Hebrew University, Jerusalem, Israel 91904: E. Gafni, 3732 Boelter Hall, Computer Science Department, U. C. L.A., Los Angeles. Cahfornia 90024. M. Merritt, 600 Mountain Ave., Murray Hill. NJ 07974; N. Shavit, Laborato~ for Computer Scienee, MIT NE43, 367 Technology Square, Cambridge MA 02139. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice N gwen that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission. (!2 1993 ACM 0004-541 1/93/0900-0873 $01.50 Joumd of ihe Amocl.]tmn for Computmg Mdchmerv, Vd 40. No 4. Scptemhcr 1993. pp 873-89[1

Methods and apparatus for protecting against and/or responding to an overload condition at a node ('victim') (H0-H4) in a distributed network divert traffic otherwise destined for the victim to one or more other nodes, which can filter the diverted traffic, passing a portion of it to the victim, and/or effect processing of one or more of the diverted packets on behalf of the victim. Diversion can be performed by one or more nodes (collectively, a 'first set' of nodes) (R0-R8) external to the victim. Filtering and/or effecting traffic processing can be performed by one or more nodes (collectively, a 'second set' of nodes) (G0-G3) also external to the victim. Those first and second sets can have zero, one or more nodes in common or, put another way, they may wholly, partially or not overlap. The methods and apparatus have application in protecting nodes in a distributed network, such as the Internet, against distributed denial of service (DDoS) attacks.

Methods and apparatus for protecting against overload conditions on nodes of a distributed network

A method for authenticating communication traffic includes receiving a first request, such as a DNS request, sent over a network from a source address, to provide network information regarding a given domain name. A response is sent to the source address in reply to the first request. When a second request is from the source address in reply to the response, the authenticity of the first request is assessed based on the second request.

Protecting against spoofed DNS messages

Computational and biological systems are often distributed so that processors (cells) jointly solve a task, without any of them receiving all inputs or observing all outputs. Maximal independent set (MIS) selection is a fundamental distributed computing procedure that seeks to elect a set of local leaders in a network. A variant of this problem is solved during the development of the fly's nervous system, when sensory organ precursor (SOP) cells are chosen. By studying SOP selection, we derived a fast algorithm for MIS selection that combines two attractive features. First, processors do not need to know their degree; second, it has an optimal message complexity while only using one-bit messages. Our findings suggest that simple and efficient algorithms can be developed on the basis of biologically derived insights.

/pdf/a-biological-solution-to-a-fundamental-distributed-computing-4qt00mobu6.pdf

Yehuda Afek

Papers

Atomic snapshots of shared memory

Atomic snapshots of shared memory

Methods and apparatus for protecting against overload conditions on nodes of a distributed network

Protecting against spoofed DNS messages

A Biological Solution to a Fundamental Distributed Computing Problem