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

A distributed algorithm is adaptive if the worst case step complexity of its operations is bounded by a function of the number of processes that are concurrently active during the operation (rather than a function of N, the total number of processes, which is usually much larger). We present long-lived and adaptive algorithms for collect in the read/write shared-memory model. Replacing the reads and writes in long-lived shared memory algorithms with our adaptive collect results in many cases in a corresponding long-lived algorithm which is adaptive. Examples of such applications, which are discussed are atomic-snapshots, and l-exclusion. Following the long-lived and adaptive collect we present a more pragmatic version of collect, called active set. This algorithm is slightly weaker than the collect but has several advantages. We employ this algorithm to transform algorithms, such as the Bakery algorithm, into their corresponding adaptive long-lived version, which is more efficient than the version that was obtained with the collect. Previously, long-lived and adaptive algorithms in this model were presented only for the renaming problem.

Long-lived adaptive collect with applications

Layered communication protocols frequently implement a FIFO message fiacility cm top of an unrehable non-FIFO serwce such as that provided hy a packet-swltchmg network. This paper investigates the possibdity of Implementing a reliable message layer on top of an underlying layer that can low packets and deliver them out of order, with the addltlonzd restriction that the implementatmn uses only a fixed fimte number of different packets. A new formalism is presented to spcclfy communication layers and their properties, the notion of their implementation by 1/0 automata. and the properties of such implementations. An 1/0 automaton that Implements a rellable layer over an unreliable layer is presented In this implementation, tbe number ot packets needed to deliver each succeeding message increases permanently as additional packet-loss and reordering faults occur. A proof is gwen that no protocol can avoid such performance degradatmn.

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Reliable communication over unreliable channels

A constant space algorithm for rate based flow control in large computer networks. The switches in the network dynamically measure their unused link capacity, and signal sessions with higher rates to reduce their rates to that unused link capacity. Sessions with lower rates are allowed to increase their rates. This algorithm is suitable for both ATM networks and suitably modified TCP networks.

Flow control algorithm for high speed networks

A local stabilizer protocol that takes any on-line or of-line distributed algorithm and converts it into a synchronous self-stabilizing algorithm with local monitoring and repairing properties is presented. Whenever the self-stabilizing version enters an inconsistent state, the inconsistency is detected, in O(1) time, and the system state is repaired in a local manner. The expected computation time that is lost during the repair process is proportional to the largest diameter of a faulty region.

Local stabilizer

This paper addresses the problem of distributively electing a leader in both syn- chronous and asynchronous complete networks. O(n log n) messages synchronous and asynchronous algorithms are presented. The time complexity of the synchronous algorithm is O(log n), while that of the asynchronous algorithm is O(n). In the synchronous case, a lower bound of 12(nlogn) on the message complexity is proven. It is also proven that any message-optimal synchronous algo- rithm requires 12(log n) time. In proving these bounds, the type of operations performed by nodes are not restricted. The bounds thus apply to general algorithms and not just to comparison-based algorithms. to be selected from a set of nodes which initially differ only in their identifiers (ids), with no node being aware of any other id. An arbitrary subset of nodes wakes up spontaneously at arbitrary times and starts the algorithm by sending messages over the network. When the message exchange terminates, a leader is distinguished from all other nodes. This paper specializes in the problem of electing a leader in a complete network. In such a network, each pair of nodes is connected by a bidirectional communication link. Before the algorithm starts, no node has any information on any of the other nodes. Hence, the incident links of a node, on which no message was sent or received, are indistinguishable. We consider both synchronous and asynchronous modes of

Yehuda Afek

Papers

Long-lived adaptive collect with applications

Reliable communication over unreliable channels

Flow control algorithm for high speed networks

Local stabilizer

Time and message bounds for election in synchronous and asynchronous complete networks