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 study the group renaming task, which is a natural generalization of the renaming task. An instance of this task consists of n processors, partitioned into m groups, each of at most g processors. Each processor knows the name of its group, which is in { 1, ..., M }. The task of each processor is to choose a new name for its group such that processors from different groups choose different new names from {1, ..., ***}, where ***< M . We consider two variants of the problem: a tight variant, in which processors of the same group must choose the same new group name, and a loose variant, in which processors from the same group may choose different names. Our findings can be briefly summarized as follows:

 1 We present an algorithm that solves the tight variant of the problem with ***= 2m *** 1 in a system consisting of g -consensus objects and atomic read/write registers. In addition, we prove that it is impossible to solve this problem in a system having only (g *** 1)-consensus objects and atomic read/write registers.
 1 We devise an algorithm for the loose variant of the problem that only uses atomic read/write registers, and has $\ell = 3n - \sqrt{n} - 1$. The algorithm also guarantees that the number of different new group names chosen by processors from the same group is at most $\min\{g, 2m, 2\sqrt{n}\}$. Furthermore, we consider the special case when the groups are uniform in size and show that our algorithm is self-adjusting to have ***= m (m + 1) / 2, when $m , and $\ell = 3n / 2 + m - \sqrt{n}/2 - 1$, otherwise.

Group Renaming

We consider the power of objects in the unbounded concurrency shared memory model, where there is an infinite set of processes and the number of processes active concurrently may increase without bound. By studying this model we obtain new results and observations that are relevant and meaningful to the standard bounded concurrency model.First we resolve an open problem from 2006 and provide, contrary to what was conjectured, an unbounded concurrency wait-free implementation of a swap object from 2-consensus objects. This construction resolves another puzzle that has eluded us for a long time, that of considerably simplifying a 16 year old complicated bounded concurrency swap construction.A further insight to the traditional bounded concurrency model that we obtain by studying the unbounded concurrency model, is a refinement of the top level of the wait-free hierarchy, the class of infinite-consensus number objects. First we resolve an open question of Merritt and Taubenfeld from 2003, showing that having n-consensus objects for all n does not imply consensus under unbounded concurrency. I.e., consensus alone, treated as a black box, cannot be "boosted" in this way. We continue to show an infinite-number consensus object that while able to perform consensus for any n-bounded concurrency (n unknown in advance) cannot solve consensus in the face of unbounded concurrency. This divides the infinite-consensus class of objects into two, those that can solve consensus for unbounded concurrency, and those that cannot.

/pdf/from-bounded-to-unbounded-concurrency-objects-and-back-39gzqbmcze.pdf

From bounded to unbounded concurrency objects and back

This paper explores the power of failure detectors in read write shared memory systems with n processes whose names are drawn from the set {1...m}, m>=2n-1. We do so by making an additional assumption, name obliviousness, on top of the three failure detector assumptions introduced by ZieliDski. We present name non-oblivious failure detectors that are strong enough to wait-free solve the Symmetry Breaking (SB) problem, but not enough to solve the (n-1)-Set Consensus problem. Furthermore a family of weakest such failure detectors is presented. On the other hand we show that any non trivial name oblivious failure detector can wait-free solve (n-1)-Set Consensus, by introducing a simple extension to anti-Omega, the Loose-anti-Omega failure detector, and proving that it is the weakest failure detector that conforms to the four assumptions above.

Failure detectors in loosely named systems

We present a basic tool for zero day attack signature extraction. Given two large sets of messages, P of messages captured in the network at peacetime (i.e., mostly legitimate traffic) and $A$ captured during attack time (i.e., contains many attack messages), we present a tool for extracting a set $S$ of strings, that are frequently found in A and not in P. Therefore, a packet containing one of the strings from S is likely to be an attack packet.This is an important tool in protecting sites on the Internet from Worm attacks, and Distributed Denial of Service (DDoS) attacks. It may also be useful for other problems, including command and control identification, DNA-sequences analysis, etc. which are beyond the scope of this work.Two contributions of this paper are the system we developed to extract the required signatures together with the problem definition and the string-heavy hitters algorithm. This algorithm finds popular strings of variable length in a set of messages, using, in a tricky way, the classic heavy-hitter algorithm as a building block. This algorithm is then used by our system to extract the desired signatures. Using our system a yet unknown attack can be detected and stopped within minutes from attack start time.

https://www.faculty.idc.ac.il/bremler/Papers/AutoSigExtraction.pdf

Automated signature extraction for high volume attacks

This paper takes advantage of the emerging multi-core computer architecture to design a general framework for mitigating network-based complexity attacks. In complexity attacks, an attacker carefully crafts "heavy" messages (or packets) such that each heavy message consumes substantially more resources than a normal message. Then, it sends a sufficient number of heavy messages to bring the system to a crawl at best. In our architecture, called MCA2---Multi-Core Architecture for Mitigating Complexity Attacks---cores quickly identify such suspicious messages and divert them to a fraction of the cores that are dedicated to handle all the heavy messages. This keeps the rest of the cores relatively unaffected and free to provide the legitimate traffic the same quality of service as if no attack takes place.We demonstrate the effectiveness of our architecture by examining cache-miss complexity attacks against Deep Packet Inspection (DPI) engines. For example, for Snort DPI engine, an attack in which 30% of the packets are malicious degrades the system throughput by over 50%, while with MCA2 the throughput drops by either 20% when no packets are dropped or by 10% in case dropping of heavy packets is allowed. At 60% malicious packets, the corresponding numbers are 70%, 40% and 23%.

/pdf/mca-2-multi-core-architecture-for-mitigating-complexity-3eyqp7ice9.pdf

Yehuda Afek

Papers

Group Renaming

From bounded to unbounded concurrency objects and back

Failure detectors in loosely named systems

Automated signature extraction for high volume attacks

MCA 2 : Multi-Core Architecture for Mitigating Complexity Attacks