Improved description and monitoring of near surface hazardous infiltrate complexes by shear waves for effective containment reponse

Among numerous causes of fluid releases and infiltration in near surface, resurgence in such anthropic activities associated with unconventional resource developments have brought about a resounding concern. Apart from the risk of an immediate chemical hazard, a long term possible recurrent geo-environmental risk since can also be envisaged as for various prevalent stake holders and broader initiatives. Urgency and exactness for spatiotemporal containment and remediation promotes the devising of efficient methods for monitoring near subsurface flow complexes caused by such spills. Swave (Shear waves) spectral imaging results, in relevant context, of a controlled immiscible fluid displacement monitoring experimental study are analysed and inferred. Against the prospective method as well evaluated, Swave diffraction associated spectral peculiarities are examined, importantly, given background medium characteristics definitions invoking fresh insights of microscale significance alongside macroscale potential

Different approaches to community detection

A precise definition of what constitutes a community in networks has remained elusive. Consequently, network scientists have compared community detection algorithms on benchmark networks with a particular form of community structure and classified them based on the mathematical techniques they employ. However, this comparison can be misleading because apparent similarities in their mathematical machinery can disguise different reasons for why we would want to employ community detection in the first place. Here we provide a focused review of these different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different approaches to community detection also delineates the many lines of research and points out open directions and avenues for future research.Comment: 14 pages, 2 figures. Written as a chapter for forthcoming Advances in network clustering and blockmodeling, and based on an extended version of The many facets of community detection in complex networks, Appl. Netw. Sci. 2: 4 (2017) by the same author

Learning the Preferences of Ignorant, Inconsistent Agents

An important use of machine learning is to learn what people value. What posts or photos should a user be shown? Which jobs or activities would a person find rewarding? In each case, observations of people's past choices can inform our inferences about their likes and preferences. If we assume that choices are approximately optimal according to some utility function, we can treat preference inference as Bayesian inverse planning. That is, given a prior on utility functions and some observed choices, we invert an optimal decision-making process to infer a posterior distribution on utility functions. However, people often deviate from approximate optimality. They have false beliefs, their planning is sub-optimal, and their choices may be temporally inconsistent due to hyperbolic discounting and other biases. We demonstrate how to incorporate these deviations into algorithms for preference inference by constructing generative models of planning for agents who are subject to false beliefs and time inconsistency. We explore the inferences these models make about preferences, beliefs, and biases. We present a behavioral experiment in which human subjects perform preference inference given the same observations of choices as our model. Results show that human subjects (like our model) explain choices in terms of systematic deviations from optimal behavior and suggest that they take such deviations into account when inferring preferences.Comment: AAAI 201

Inferring Temporal Behaviours Through Kernel Tracing

In order to provide reliable system support for real-time applications, it is often important to be able to collect statistics about the tasks temporal behaviours (in terms of execution times and inter-arrival times). Such statistics can, for example, be used to provide a-priori schedulability guarantees, or to perform some kind of on-line adaptation of the scheduling parameters (adaptive scheduling, or feedback scheduling). This work shows how the Linux kernel allows to collect such statistics by using an internal function tracer called Ftrace. Based on this feature, tools can be developed to evaluate the real-time performance of a system or an application, to debug real-time applications, and/or to infer the temporal properties (for example, periodicity) of tasks running in the system

Beyond Counting: New Perspectives on the Active IPv4 Address Space

In this study, we report on techniques and analyses that enable us to capture Internet-wide activity at individual IP address-level granularity by relying on server logs of a large commercial content delivery network (CDN) that serves close to 3 trillion HTTP requests on a daily basis. Across the whole of 2015, these logs recorded client activity involving 1.2 billion unique IPv4 addresses, the highest ever measured, in agreement with recent estimates. Monthly client IPv4 address counts showed constant growth for years prior, but since 2014, the IPv4 count has stagnated while IPv6 counts have grown. Thus, it seems we have entered an era marked by increased complexity, one in which the sole enumeration of active IPv4 addresses is of little use to characterize recent growth of the Internet as a whole. With this observation in mind, we consider new points of view in the study of global IPv4 address activity. Our analysis shows significant churn in active IPv4 addresses: the set of active IPv4 addresses varies by as much as 25% over the course of a year. Second, by looking across the active addresses in a prefix, we are able to identify and attribute activity patterns to network restructurings, user behaviors, and, in particular, various address assignment practices. Third, by combining spatio-temporal measures of address utilization with measures of traffic volume, and sampling-based estimates of relative host counts, we present novel perspectives on worldwide IPv4 address activity, including empirical observation of under-utilization in some areas, and complete utilization, or exhaustion, in others.Comment: in Proceedings of ACM IMC 201