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 studies a variant of the leader election problem under the stone age model (Emek and Wattenhofer, PODC 2013) that considers a network of n randomized finite automata with very weak communication capabilities (a multi-frequency asynchronous generalization of the beeping model's communication scheme). Since solving the classic leader election problem is impossible even in more powerful models, we consider a relaxed variant, referred to as k-leader selection, in which a leader should be selected out of at most k initial candidates. Our main contribution is an algorithm that solves k-leader selection for bounded k in the aforementioned stone age model. On (general topology) graphs of diameter D, this algorithm runs in O~(D) time and succeeds with high probability. The assumption that k is bounded turns out to be unavoidable: we prove that if k = omega (1), then no algorithm in this model can solve k-leader selection with a (positive) constant probability.

https://drops.dagstuhl.de/opus/volltexte/2018/9793/pdf/LIPIcs-DISC-2018-4.pdf/

Selecting a Leader in a Network of Finite State Machines.

A simple, general and optimal procedure to adapt algorithms designed for fixed topology networks to run on a network with dynamically changing topology is presented. The communication and time complexities of the procedure, per topological change, are independent of the number of topological changes and are linearly bounded by the size of the subnetwork over which the algorithm is running.

Local Fail-safe Network Reset Procedure

Synchronization power depends on the register size (Preliminary Version)

Until now, distributed algorithms for rational agents have assumed a-priori knowledge of $n$, the size of the network. This assumption is challenged here by proving how much a-priori knowledge is necessary for equilibrium in different distributed computing problems. Duplication - pretending to be more than one agent - is the main tool used by agents to deviate and increase their utility when not enough knowledge about $n$ is given. The a-priori knowledge of $n$ is formalized as a Bayesian setting where at the beginning of the algorithm agents only know a prior $\sigma$, a distribution from which they know $n$ originates. We begin by providing new algorithms for the Knowledge Sharing and Coloring problems when $n$ is a-priori known to all agents. We then prove that when agents have no a-priori knowledge of $n$, i.e., the support for $\sigma$ is infinite, equilibrium is impossible for the Knowledge Sharing problem. Finally, we consider priors with finite support and find bounds on the necessary interval $[\alpha,\beta]$ that contains the support of $\sigma$, i.e., $\alpha \leq n \leq \beta$, for which we have an equilibrium. When possible, we extend these bounds to hold for any possible protocol.

Yehuda Afek

Papers

Selecting a Leader in a Network of Finite State Machines.

Local Fail-safe Network Reset Procedure

Synchronization power depends on the register size (Preliminary Version)

The Role of A-priori Information in Networks of Rational Agents

Upper and Lower Bounds for Routing Schemes in Dynamic Networks (Abstract)