Data consistency is a critical topic in distributed systems. In existing consistency models, causal consistency has attracted a significant amount of attention because it can satisfy high﹑erformance requirement
In this work, we present Antipode, a bolt-on technique for preventing cross-service consistency violations in distributed applications. It enforces cross-service consistency by propagating lineages of datastore operations both alongside end-to-end requests and within datast...
The ideas in this paper–Don’t Settle for Eventual: Scalable Causal Consistency for Wide-Area Storage with COPS–are different. It’s not another eventually consistent system, or a traditional transaction oriented system, or a replication based system, or a system that punts on the issue. It...
A real network is usually a complex nonlinear system, whose causal structure is generally difficult to be inferred. We focus on gene regulatory networks in biological systems, and apply the CVP algorithm to five real datasets of gene regulatory networks, i.e. the SOS DNA repair network data, ...
1. Strength of associationAs FoG episodes occur, the value of the FI in higher than that when normal gait is happening. 2. TemporalityFoG in the vast majority of cases occurs when the FI increases. 3. ConsistencySeveral studies were applied on different patients, which produced the same resul...
This is fundamental to ensure causal consistency: on one side this ensures we go back to a past state that could have been reached by a forward execution, on the other side we undo 374 E. Giachino, I. Lanese, and C.A. Mezzina the minimal number of actions needed to reach this aim...
We have also repeated the experiments multiple times to ensure the reliability and consistency of our results. The conclusion threat to validity of our framework is related to the types of anomalies used in experiments. As microservice applications have a variety of performance anomalies that can ...
[pdf] (2021 NeurIPS) Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning. Maxwell Nye, Michael Henry Tessler, Joshua B. Tenenbaum, Brenden M. Lake. [pdf]Related Non-NLP Papers(2021 arXiv) Desiderata for Representation Learning: A Causal ...
In this factored MDP framework, nodes represent system variables (including rewards and different dimensions of states and actions), while edges denote their relationships within the MDP. We learn the causal structure by learning the causal matrices MM to mask irrelevant factors in the factored MDP,...
However, the criteria for determining the lumpability primarily focus on the consistency of the Markov dynamics rather than the causality assessed by EI, and the reversibility they are concerned with is not dynamical reversibility27. Therefore, the concepts explored in this paper serve as a ...