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

We consider shared memory systems that support multiobject operations in which processes may simultaneously access several objects in one atomic operation. We provide upper and lower bounds on the synchronization power (consensus number) of multiobject systems as a function of the type and the number of objects that may be simultaneously accessed in one atomic operation. These bounds imply that known classifications of component objects fail to characterize the synchronization power of their combination. In particular, we show that in the context of multiobjects, fetch & add objects are less powerful than swap objects, which in turn are less powerful than queue objects. This stands in contrast to the fact that swap can be implemented from fetch & add. Herein we introduce a restricted notion of implementation, called direct implementation. We show that, if objects of type Y have a direct implementation from objects of type X, then Y-based multiobjects can also be implemented from X-based multiobjects. Using this observation, we derive results such as: there are no direct implementations of swap or queue objects from any collection of commutative objects (e.g., fetch & add, test & set).

https://www.faculty.idc.ac.il/gadi/MyPapers/1999AMT-multiobjects.pdf

The power of multiobjects

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.

https://ieeexplore.ieee.org/iel3/4625/13001/00595159.pdf

Local stabilizer

Interrupting snapshots and the JavaTM size method

Motivated by a recent new type of randomized Distributed Denial of Service (DDoS) attacks on the Domain Name Service (DNS), we develop novel and efficient distinct heavy hitters algorithms and build an attack identification system that uses our algorithms. Heavy hitter detection in streams is a fundamental problem with many applications, including detecting certain DDoS attacks and anomalies. A (classic) heavy hitter (HH) in a stream of elements is a key (e.g., the domain of a query) which appears in many elements (e.g., requests). When stream elements consist of a pairs, ( ) a distinct heavy hitter (dhh) is a key that is paired with a large number of different subkeys. Our dHH algorithms are considerably more practical than previous algorithms. Specifically the new fixed-size algorithms are simple to code and with asymptotically optimal space accuracy tradeoffs. In addition we introduce a new measure, a combined heavy hitter (cHH), which is a key with a large combination of distinct and classic weights. Efficient algorithms are also presented for cHH detection. Finally, we perform extensive experimental evaluation on real DNS attack traces, demonstrating the effectiveness of both our algorithms and our DNS malicious queries identification system.

Efficient Distinct Heavy Hitters for DNS DDoS Attack Detection.

Working on shared mutable data requires synchronization through barriers, locks or transactional memory mechanisms. To avoid this overhead a thread may privatize part of the data and work on it locally. By privatizing a data item a thread is guaranteed that it is the only one accessing this data, i.e., that it accesses the data item in exclusion.

The most robust and yet lock-free privatization algorithms, are lockfree reference counting (LFRC). These algorithms attach a counter to each node, which counts the number of references to the node. However, these counters are shared by all threads in the system and thus are contention prone, and must be updated with expensive atomic operations such as CAS.

We present a new privatization algorithm, Public Guard (PG); an algorithm which eliminates most of the contention of LFRC algorithms, while maintaining their robustness and non blocking nature. Our evaluation shows that PG improves performance by up to 50% in many work loads.

Another problematic issue with LFRC, that we address in this paper, is that a counter of a private node, may be accessed by a slow thread. This may prevent LFRC from freeing memory to the system. In another contribution of this paper we suggest a method with minimal overhead to allow LFRC to reclaim memory.

http://mcg.cs.tau.ac.il/papers/opodis2010-privatization.pdf

Yehuda Afek

Papers

The power of multiobjects

Local stabilizer

Interrupting snapshots and the JavaTM size method

Efficient Distinct Heavy Hitters for DNS DDoS Attack Detection.

Efficient lock free privatization