On reducing the complexity of matrix clocks

Matrix clocks are a generalization of the notion of vector clocks that allows the local representation of causal precedence to reach into an asynchronous distributed computation's past with depth $x$, where $x\ge 1$ is an integer. Maintaining matrix clocks correctly in a system of $n$ nodes requires that everymessage be accompanied by $O(n^x)$ numbers, which reflects an exponential dependency of the complexity of matrix clocks upon the desired depth $x$. We introduce a novel type of matrix clock, one that requires only $nx$ numbers to be attached to each message while maintaining what for many applications may be the most significant portion of the information that the original matrix clock carries. In order to illustrate the new clock's applicability, we demonstrate its use in the monitoring of certain resource-sharing computations

Reflections on the Continued Popularity of the Transtheoretical Model

Sometimes in health behavior research, purportedly novel theoretical models are offered that may help move thinking about behavior change forward, and may be well-received by clinicians, but over time end up being a subject of continual scientific disconfirmation. The transtheoretical model is one rather popular example. The five stages of change and associated 10 processes of change are well-known (e.g., DiClemente et al., 2008; DiClemente et al., 2004). Yet, the model has been discredited to a large extent by several in the health behavior research community. In the present editorial, we briefly summarize the transtheoretical model, mention its current ongoing popularity, identify the critiques of this model, and suggest how one might best consider the model in future health behavior research and practice

An order-based algorithm for implementing multiparty synchronization

Multiparty interactions are a powerful mechanism for coordinating several entities that need to cooperate in order to achieve a common goal. In this paper, we present an algorithm for implementing them that improves on previous results in that it does not require the whole set of entities or interactions to be known at compile- or run-time, and it can deal with both terminating and non-terminating systems. We also present a comprehensive simulation analysis that shows how sensitive to changes our algorithm is, and compare the results with well-known proposals by other authors. This study proves that our algorithm still performs comparably to other proposals in which the set of entities and interactions is known beforehand, but outperforms them in some situations that are clearly identified. In addition, these results prove that our algorithm can be combined with a technique called synchrony loosening without having an effect on efficiency.Ministerio de Ciencia y Tecnología TIC 2003-02737-C02-0

Dining philosophers with masking tolerance to crash faults

We examine the tolerance of dining philosopher algorithms subject to process crash faults in arbitrary conflict graphs. This classic problem is unsolvable in asynchronous message-passing systems subject to even a single crash fault. By contrast, dining can be solved in synchronous systems capable of implementing the perfect failure detector P (from the Chandra-Toueg hierarchy). We show that dining is also solvable in weaker timing models using a combination of the trusting detector T and the strong detector S; Our approach extends and composes two currents of previous research. First, we define a parametric generalization of Lynch’s classic algorithm for hierarchical resource allocation. Our construction converts any mutual exclusion algorithm into a valid dining algorithm. Second, we consider the fault-tolerant mutual exclusion algorithm (FTME) of Delporte-Gallet, et al., which uses T and the strong detector S to mask crash faults in any environment. We instantiate our dining construction with FTME, and prove that the resulting dining algorithm guarantees masking tolerance to crash faults. Our contribution (1) defines a new construction for transforming mutual exclusion algorithms into dining algorithms, and (2) demonstrates a better upper-bound on the fault-detection capabilities necessary to mask crash faults in dining philosophers

A DEEP STUDY ON THE CONCEPT OF DIGITAL ETHICS

From internet governance to teleworking, from digital exclusion to privacy and computer crimes, there are various issues that can be listed as a part of what Digital Ethics - the “ethics of computer era” - is and involves. Before analyzing some of these issues of great importance and relevance nowadays, we need to ask if there is a common factor, a “unifying principle”, for Digital Ethics

A Causal Interpretation of Selection Theory

The following dissertation is an inferentialist account of classical population genetics. I present the theory as a definite body of interconnected inferential rules for generating mathematical models of population dynamics. To state those rules, I use the notion of causation as a primitive. First, I put forward a rule stating the circumstances of application of the theory, one that uses causal language to pick out the types of entities over which the theory may be deployed. Next, I offer a rule for grouping such entities into populations based on their competitive causal relationships. Then I offer a general algorithm for generating classical population genetics models for such populations on the basis of what causal influences operate within them.Dynamical models in population genetics are designed to demystify natural phenomena, chiefly to show how adaptation, altruism, and genetic polymorphism can be explained in terms of natural rather than supernatural processes. In order for the theory to serve this purpose, it must be possible to understand, in a principled fashion, when and how to deploy the theory. By presenting the theory as a system of ordered inferential rules that takes causal information as its critical input and yields dynamical models as its outputs, I show explicitly how classical population genetics functions as a non-circular theoretical apparatus for generating explanations. The generalization of the theory achieved by presenting it using causal vocabulary shows how the scope of the theory of natural selection extends beyond its traditional domain of systems distinguished by genetic variations

Decentralized Task and Path Planning for Multi-Robot Systems

We consider a multi-robot system with a team of collaborative robots and multiple tasks that emerges over time. We propose a fully decentralized task and path planning (DTPP) framework consisting of a task allocation module and a localized path planning module. Each task is modeled as a Markov Decision Process (MDP) or a Mixed Observed Markov Decision Process (MOMDP) depending on whether full states or partial states are observable. The task allocation module then aims at maximizing the expected pure reward (reward minus cost) of the robotic team. We fuse the Markov model into a factor graph formulation so that the task allocation can be decentrally solved using the max-sum algorithm. Each robot agent follows the optimal policy synthesized for the Markov model and we propose a localized forward dynamic programming scheme that resolves conflicts between agents and avoids collisions. The proposed framework is demonstrated with high fidelity ROS simulations and experiments with multiple ground robots

Hydrology

In this book, an attempt is made to highlight the recent advances in Hydrology. The several topics examined in this book form the underpinnings of larger-scale considerations, including but not limited to topics such as large-scale hydrologic processes and the evolving field of Critical Zone Hydrology. Computational modeling, data collection, and visualization are additional subjects, among others, examined in the set of topics presented