Causal Memory Research Articles

Distributed Asynchronous Games With Causal Memory are Undecidable

We show the undecidability of the distributed control problem when the plant is an asynchronous automaton, the controllers use causal memory and the goal of the controllers is to put each process in a local accepting state.

Distributed causal memory: modular specification and verification in higher-order distributed separation logic

We present the first specification and verification of an implementation of a causally-consistent distributed database that supports modular verification of full functional correctness properties of clients and servers. We specify and reason about the causally-consistent distributed database in Aneris, a higher-order distributed separation logic for an ML-like programming language with network primitives for programming distributed systems. We demonstrate that our specifications are useful, by proving the correctness of small, but tricky, synthetic examples involving causal dependency and by verifying a session manager library implemented on top of the distributed database. We use Aneris's facilities for modular specification and verification to obtain a highly modular development, where each component is verified in isolation, relying only on the specifications (not the implementations) of other components. We have used the Coq formalization of the Aneris logic to formalize all the results presented in the paper in the Coq proof assistant.

Attention-Based SeriesNet: An Attention-Based Hybrid Neural Network Model for Conditional Time Series Forecasting

Traditional time series forecasting techniques can not extract good enough sequence data features, and their accuracies are limited. The deep learning structure SeriesNet is an advanced method, which adopts hybrid neural networks, including dilated causal convolutional neural network (DC-CNN) and Long-short term memory recurrent neural network (LSTM-RNN), to learn multi-range and multi-level features from multi-conditional time series with higher accuracy. However, they didn’t consider the attention mechanisms to learn temporal features. Besides, the conditioning method for CNN and RNN is not specific, and the number of parameters in each layer is tremendous. This paper proposes the conditioning method for two types of neural networks, and respectively uses the gated recurrent unit network (GRU) and the dilated depthwise separable temporal convolutional networks (DDSTCNs) instead of LSTM and DC-CNN for reducing the parameters. Furthermore, this paper presents the lightweight RNN-based hidden state attention module (HSAM) combined with the proposed CNN-based convolutional block attention module (CBAM) for time series forecasting. Experimental results show our model is superior to other models from the viewpoint of forecasting accuracy and computation efficiency.

Is Remembering to do a Special Kind of Memory?

When a person decides to do something in the future, she forms an intention and her intention persists. Philosophers have thought about the rational requirement that an agent’s intention persists until its execution. But philosophers have neglected to think about the causal memory mechanisms that could enable this kind of persistence and its role in rational long-term agency. Our aim of this paper is to fill this gap by arguing that memory for intention is a specific kind of memory. We do this by evaluating and rejecting standard declarative accounts of memory for intention and arguing for the plausibility of an alternative model of memory for intention. We argue for the alternative by spelling out a number of computational principles that could enable retaining and retrieving intentions from long-term memory. These principles could explain a number of core features of intentions.

Mod en teori om kausale hukommelsesprocesser: præliminære bemærkninger

This paper argues for a theory of memory that I have coined “the theory of causal memory processes”. The central claim of the paper is that it is more empirically sound to construct theories of memory in accordance with the theory of causal processes rather than in accordance with causality as regulation. In order to put this claim forward I will, first, describe what kind of function causality serves in philosophical theories of memory. Secondly, I will show that regularity theories of causation is dominant in the causal theories of memory. Thirdly, I will account for the theory of causal processes. Lastly, I will sketch a definition of the theory of causal memory processes.

Petri games: Synthesis of distributed systems with causal memory

We present a new multiplayer game model for the interaction and the flow of information in a distributed system. The players are tokens on a Petri net. As long as the players move in independent parts of the net, they do not know of each other; when they synchronize at a joint transition, each player gets informed of the causal history of the other player. We show that for Petri games with a single environment player and an arbitrary bounded number of system players, deciding the existence of a safety strategy for the system players is EXPTIME-complete.

Causal consistency

In distributed systems where strong consistency is costly when not impossible, causal consistency provides a valuable abstraction to represent program executions as partial orders. In addition to the sequential program order of each computing entity, causal order also contains the semantic links between the events that affect the shared objects -- messages emission and reception in a communication channel, reads and writes on a shared register. Usual approaches based on semantic links are very difficult to adapt to other data types such as queues or counters because they require a specific analysis of causal dependencies for each data type. This paper presents a new approach to define causal consistency for any abstract data type based on sequential specifications. It explores, formalizes and studies the differences between three variations of causal consistency and highlights them in the light of PRAM, eventual consistency and sequential consistency: weak causal consistency, that captures the notion of causality preservation when focusing on convergence; causal convergence that mixes weak causal consistency and convergence; and causal consistency, that coincides with causal memory when applied to shared memory.

Petri Games: Synthesis of Distributed Systems with Causal Memory

We present a new multiplayer game model for the interaction and the flow of information in a distributed system. The players are tokens on a Petri net. As long as the players move in independent parts of the net, they do not know of each other; when they synchronize at a joint transition, each player gets informed of the causal history of the other player. We show that for Petri games with a single environment player and an arbitrary bounded number of system players, deciding the existence of a safety strategy for the system players is EXPTIME-complete.

Petri Games: Synthesis of Distributed Systems with Causal Memory

Petri Games: Synthesis of Distributed Systems with Causal Memory

Counterpropagation of waves with shock fronts in a nonlinear tissue-like medium

A numerical model for describing the counterpropagation of one-dimensional waves in a nonlinear medium with an arbitrary power law absorption and corresponding dispersion is developed. The model is based on general one-dimensional Navier-Stokes equations with absorption in the form of a time-domain convolution operator in the equation of state. The developed algorithm makes it possible to describe wave interactions in the presence of shock fronts in media like biological tissue. Numerical modeling is conducted by the finite difference method on a staggered grid; absorption and sound speed dispersion are taken into account using the causal memory function. The developed model is used for numerical calculations, which demonstrate the absorption and dispersion effects on nonlinear propagation of differently shaped pulses, as well as their reflection from impedance acoustic boundaries.

Optimal propagation-based protocols implementing causal memories

Ensuring causal consistency in a Distributed Shared Memory (DSM) means all operations executed at each process will be compliant to a causality order relation. This paper first introduces an optimality criterion for a protocol P, based on a complete replication of variables at each process and propagation of write updates, that enforces causal consistency. This criterion measures the capability of a protocol to update the local copy as soon as possible while respecting causal consistency. Then we present an optimal protocol built on top of a reliable broadcast communication primitive and we show how previous protocols based on complete replication presented in the literature are not optimal. Interestingly, we prove that the optimal protocol embeds a system of vector clocks which captures the read/write semantics of a causal memory. From an operational point of view, an optimal protocol strongly reduces its message buffer overhead. Simulation studies show that the optimal protocol roughly buffers a number of messages of one order of magnitude lower than non-optimal ones based on the same communication primitive.

Long and short time quantum dynamics: III. Transients

The quantum transport equations for fast transients have the structure of a Generalized Master Equations for the single-particle distribution, with causal memory terms. Nonequilibrium Green's functions are reduced to GME if the Generalized Kadanoff–Baym Ansatz is applied. This Ansatz has been used with success both to non-linear transport and to optical transients in semi-conductors; further progress is linked with its extension to a family of the Causal Ansatzes, differing primarily in renormalization of the propagators. For the switch-on non-equilibrium states, generated by a perturbation from equilibrium, the renormalization to the dark dressed Green's function followed by calculation of the induced self-energies is a productive direction. It also circumvents the problem of correlated initial conditions, far from a general solution otherwise. Such initial conditions appear as incompatible with a Causal Ansatz in general. The presently available formalism permits to study a transient process in the whole time range using the complete NGF, but making a flexible Ansatz-based reduction appropriate to the stage of dynamic evolution.

On the interconnection of causal memory systems

In this paper, we look at the interconnection of propagation-based causal Distributed shared memory (DSM) systems. We present extremely simple protocols to interconnect two such systems (possibly implemented with different algorithms), that only require the existence of a bidirectional reliable FIFO channel connecting one process from each system. We show that the resulting DSM system is also causal. This result can be used to interconnect any number of DSM propagation-based causal systems, by interconnecting them in pairs with a tree topology.

On the composability of consistency conditions

This paper presents a formal framework, which is based on the notion of a serialization set, that enables to compose a set of consistency conditions into a more restrictive one. To exemplify the utility of this framework, a list of very basic consistency conditions is identified, and it is shown that various compositions of the basic conditions yield some of the most commonly used consistency conditions, such as sequential consistency, causal memory, and Pipelined RAM. The paper also lists several applications that can benefit from even weaker semantics than Pipelined RAM that can be expressed as a composition of a small subset of the basic conditions.

A Correctness Condition for High-Performance Multiprocessors

Hybrid consistency, a consistency condition for shared memory multiprocessors, attempts to capture the guarantees provided by contemporary high-performance architectures. It combines the expressiveness of strong consistency conditions (e.g., sequential consistency, linearizability) and the efficiency of weak consistency conditions (e.g., pipelined RAM, causal memory). Memory access operations are classified as either strong or weak. A global ordering of strong operations at different processes is guaranteed, but there is very little guarantee on the ordering of weak operations at different processes, except for what is implied by their interleaving with the strong operations. A formal and precise definition of this condition is given and an algorithm for providing hybrid consistency on distributed memory machines is presented. The response time of the algorithm is proved to be within a constant multiplicative factor of the (theoretical) optimal time bounds.

Complete memory structures for approximating nonlinear discrete-time mappings

This paper introduces a general structure that is capable of approximating input-output maps of nonlinear discrete-time systems. The structure is comprised of two stages, a dynamical stage followed by a memoryless nonlinear stage. A theorem is presented which gives a simple necessary and sufficient condition for a large set of structures of this form to be capable of modeling a wide class of nonlinear discrete time systems. In particular, we introduce the concept of a "complete memory". A structure with a complete memory dynamical stage and a sufficiently powerful memoryless stage is shown to be capable of approximating arbitrarily wide class of continuous, causal, time invariant, approximately-finite-memory mappings between discrete-time signal spaces. Furthermore, we show that any bounded-input bounded output, time-invariant, causal memory structure has such an approximation capability if and only if it is a complete memory. Several examples of linear and nonlinear complete memories are presented. The proposed complete memory structure provides a template for designing a wide variety of artificial neural networks for nonlinear spatiotemporal processing.

Limitations of fast consistency conditions for distributed shared memories

A consistency condition for distributed shared memory is fast if it has a fast implementation in which the execution time of every operation is significantly faster than the network delay. These conditions include Pipelined RAM, weak consistency, causal memory, and one interpretation of processor consistency. It is shown that if a condition is fast then it does not support non-centralized solutions for mutual exclusion.

Causal memory: definitions, implementation, and programming

The abstraction of a shared memory is of growing importance in distributed computing systems. Traditional memory consistency ensures that all processes agree on a common order of all operations on memory. Unfortunately, providing these guarantees entails access latencies that prevent scaling to large systems. This paper weakens such guarantees by definingcausal memory, an abstraction that ensures that processes in a system agree on the relative ordering of operations that arecausally related. Because causal memory isweakly consistent, it admits more executions, and hence more concurrency, than either atomic or sequentially consistent memories. This paper provides a formal definition of causal memory and gives an implementation for message-passing systems. In addition, it describes a practical class of programs that, if developed for a strongly consistent memory, run correctly with causal memory.

Causal memory: definitions, implementation, and programming

The abstraction of a shared memory is of growing importance in distributed computing systems. Traditional memory consistency ensures that all processes agree on a common order of all operations on memory. Unfortunately, providing these guarantees entails access latencies that prevent scaling to large systems. This paper weakens such guarantees by definingcausal memory, an abstraction that ensures that processes in a system agree on the relative ordering of operations that arecausally related. Because causal memory isweakly consistent, it admits more executions, and hence more concurrency, than either atomic or sequentially consistent memories. This paper provides a formal definition of causal memory and gives an implementation for message-passing systems. In addition, it describes a practical class of programs that, if developed for a strongly consistent memory, run correctly with causal memory.