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

Hardware lock-elision HLE introduces concurrency into legacy lock-based code by optimistically executing critical sections in a fast-path as hardware transactions. Its main limitation is that in case of repeated aborts, it reverts to a fallback-path that acquires a serial lock. This fallback-path lacks hardware-software concurrency, because all fast-path hardware transactions abort and wait for the completion of the fallback. Software lock elision has no such limitation, but the overheads incurred are simply too high.

We propose amalgamated lock-elision ALE, a novel lock-elision algorithm that provides hardware-software concurrency and efficiency: the fallback-path executes concurrently with fast-path hardware transactions, while the common-path fast-path reads incur no overheads and proceed without any instrumentation. The key idea in ALE is to use a sequence of fine-grained locks in the fallback-path to detect conflicts with the fast-path, and at the same time reduce the costs of these locks by executing the fallback-path as a series segments, where each segment is a dynamic length short hardware transaction.

We implemented ALE into GCC and tested the new system on Intel Haswell 16-way chip that provides hardware transactions. We benchmarked linked-lists, hash-tables and red-black trees, as well as converting KyotoCacheDB to use ALE in GCC, and all show that ALE significantly outperforms HLE.

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Amalgamated Lock-Elision

We address the problem of solving a task T = (T 1 T m ) (called (m, 1)-BG), in which a processor returns in an arbitrary one of m simultaneous consensus subtasks T 1 ,...T m . Processor p i submits to T an input vector of proposals (prop i,1 ,...,prop i,m ), one entry per subtask, and outputs, from just one subtask l, a pair (l, prop j,l ) for some j. All processors that output at l output the same proposal. Let d be a bound on the number of distinct input vectors that may be submitted to T. For example, d = 3 if Democrats always vote Democrats across the board, and similarly for Republicans and Libertarians. A wait-free algorithm that immaterial of the number of processors solves T provided m > d is presented. In addition, if in each Tj we allow k-set consensus rather than consensus, i.e., for each l, the outputs satisfy …|j |prop j,l } [d/k]. What is the power of T = (T 1,..., T m ) when given as a subroutine, to be used by any number of processors with any number of input vectors? Obviously, T solves m-set consensus since each processor p i can submit the vector (id i , id i, ...id i ,). but can m-set consensus solve T? We show it does, and thus simultaneous consensus is a new characterization of set-consensus. Finally, what if each T j is just a binary-consensus rather than consensus? Then we get the novel problem that was recently introduced of the Committee-Decision. It was shown that for 3 processors and m = 2, the simultaneous binary-consensus is equivalent to (3, 2)-set consensus. Here, using a variation of our wait-free algorithms mentioned above, we show that a task, in which a processor is required to return in one of m simultaneous binary-consensus subtasks, when used by n processors, is equivalent to (n,m)-set consensus. Thus, while set-consensus unlike consensus, has no binary version, now that we characterize m-set consensus through simultaneous consensus, the notion of binary-set-consensus is well defined. We have then showed that binary-set-consensus is equivalent to set consensus as it was with consensus.

Simultaneous consensus tasks : A tighter characterization of set-consensus

The problem of applying static distributed algorithms in an eventually connected network is addressed. We propose a new technique, bootstrap resynchronization, by which any static diffusing computations can be reliably adapted to such a network. The communication overhead for each execution of the diffusing computation adapted by our technique is O(nE) messages, were n and 1? are the total number of nodes and links in the net work. The time overhead of the bootstrap resynchronization per execution is 0(TZ2 ). One of the simplest diffusing computations is flood. Flood can be used to send a data item in the end-to-end problem, and to receive its acknowledgment in a return flood. Applying our technique to this algorithm results in an O(n13) messages per data item, data oblivious end-to-end protocol (0(13 *Supported by NSF Presidential Young Investigator Award under grant DCR84-51396 & matching funds from XEROX Co. under grant W881111. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission. @ 1991 ACM 0-89791-439-2/91/0007/0295 $1.50 D+ nE log n) bits of communication per data item of size D bits).

Bootstrap network resynchronization (extended abstract)

The Instancy of Snapshots and Commuting Objects

A new scalable ISP level system architecture to secure and protect all IoT devices in a large number of homes is presented. The system is based on whitelisting, as in the Manufacturer Usage Description (MUD) framework, implemented as a VNF. Unlike common MUD suggestions that place the whitelist application at the home/enterprise network, our approach is to place the enforcement upstream at the provider network, combining an NFV (Network Function Virtualization) with router/switching filtering capabilities, e.g., ACLs. The VNF monitors many home networks simultaneously, and therefore, is a highly-scalable managed service solution that provides both the end customers and the ISP with excellent visibility and security of the IoT devices at the customer premises. 
The system includes a mechanism to distinguish between flows of different devices at the ISP level despite the fact that most home networks (and their IoT devices) are behind a NAT and all the flows from the same home come out with the same source IP address. Moreover, the NFV system needs to receive only the first packet of each connection at the VNF, and rules space is proportional to the number of unique types of IoT devices rather than the number of IoT devices. The monitoring part of the solution is off the critical path and can also uniquely protect from incoming DDoS attacks. 
To cope with internal traffic, that is not visible outside the customer premise and often consists of P2P communication, we suggest a hybrid approach, where we deploy a lightweight component at the CPE, whose sole purpose is to monitor P2P communication. As current MUD solution does not provide a secure solution to P2P communication, we also extend the MUD protocol to deal also with peer-to-peer communicating devices. A PoC with a large national level ISP proves that our technology works as expected.

Yehuda Afek

Papers

Amalgamated Lock-Elision

Simultaneous consensus tasks : A tighter characterization of set-consensus

Bootstrap network resynchronization (extended abstract)

The Instancy of Snapshots and Commuting Objects

NFV-based IoT Security for Home Networks using MUD