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

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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

Power supply, a surprisingly simple and new general paradigm for the development of self-stabilizing algorithms in different models, is introduced. The paradigm is exemplified by developing simple and efficient self-stabilizing algorithms for leader election and either breadth-first search or depth-first search spanning-tree constructions, in strongly connected unidirectional and bidirectional dynamic networks (synchronous and asynchronous). The different algorithms stabilize in O(n) time in both synchronous and asynchronous networks without assuming any knowledge of the network topology or size, where n is the total number of nodes. Following the leader election algorithms, we present a generic self-stabilizing spanning tree and/or leader election algorithm that produces a whole spectrum of new and efficient algorithms for these problems. Two variations that produce either a rooted depth-first search tree or a rooted breadth-first search tree are presented.

Self-Stabilizing Unidirectional Network Algorithms by Power Supply.

Labovitz et al. (2001) and Labovitz et al. (2000) noticed that sometimes it takes border gateway protocol (BGP) a substantial amount of time and messages to converge and stabilize following the failure of some node in the Internet. In this paper, we suggest a minor modification to BGP that eliminates the problem pointed out and substantially reduces the convergence time and communication complexity of BGP. Roughly speaking, our modification ensures that bad news (the failure of a node/edge) propagate fast, while good news (the establishment of a new path to a destination) propagate somewhat slower. This is achieved in BGP by allowing withdrawal messages to propagate with no delay as fast as the network forward them, while announcements propagate as they do in BGP with a delay at each node of one minRouteAdver (except for the first wave of announcements). As a by product of this work, a new stateless mechanism to overcome the counting to infinity problem is provided, which compares favorably with other known stateless mechanisms (in RIP and IGRP).

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Improved BGP convergence via ghost flushing

This paper presents Phantom, a simple constant space algorithm for rate based flow control. As shown by our simulations, it converges fast to a fair rate allocation while generating a moderate queue length. While our approach can be easily implemented in ATM switches for managing ABR traffic, it is also suitable for flow control in TCP router based networks. Both the introduced overhead and the required modifications in TCP flow control systems are minimal. The implementation of this approach in TCP guarantees fairness and provides a unifying interconnection between TCP routers and ATM networks. The new algorithm easily inter-operates with current TCP flow control mechanisms and thus can be gradually introduced into installed based TCP networks.

Phantom: a simple and effective flow control scheme

Yehuda Afek* ,We describe a fast, wait-free (2k 1)-renaming algorithm which takes O(k2) time. (Where k is the contention, the number of processes actually taking steps in a given run.) The algorithm makes extensive use of tools and techniques developed by Attiya and Fouren [AF98]. Other extensions, including a fast (long-lived) atomic snapshot algorithm, are briefly discussed.

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Yehuda Afek

Papers

Self-Stabilizing Unidirectional Network Algorithms by Power Supply.

Improved BGP convergence via ghost flushing

Phantom: a simple and effective flow control scheme

Fast, wait-free (2k-1)-renaming

A Completeness Theorem for a Class of Synchronization Objects (Extended Abstract).