178,573 research outputs found
A New Foundation for the Propensity Interpretation of Fitness
The propensity interpretation of fitness (PIF) is commonly taken to be subject to a set of simple counterexamples. We argue that three of the most important of these are not counterexamples to the PIF itself, but only to the traditional mathematical model of this propensity: fitness as expected number of offspring. They fail to demonstrate that a new mathematical model of the PIF could not succeed where this older model fails. We then propose a new formalization of the PIF that avoids these (and other) counterexamples. By producing a counterexample-free model of the PIF, we call into question one of the primary motivations for adopting the statisticalist interpretation of fitness. In addition, this new model has the benefit of being more closely allied with contemporary mathematical biology than the traditional model of the PIF
Measuring, Predicting and Visualizing Short-Term Change in Word Representation and Usage in VKontakte Social Network
Language in social media is extremely dynamic: new words emerge, trend and
disappear, while the meaning of existing words can fluctuate over time. Such
dynamics are especially notable during a period of crisis. This work addresses
several important tasks of measuring, visualizing and predicting short term
text representation shift, i.e. the change in a word's contextual semantics,
and contrasting such shift with surface level word dynamics, or concept drift,
observed in social media streams. Unlike previous approaches on learning word
representations from text, we study the relationship between short-term concept
drift and representation shift on a large social media corpus - VKontakte posts
in Russian collected during the Russia-Ukraine crisis in 2014-2015. Our novel
contributions include quantitative and qualitative approaches to (1) measure
short-term representation shift and contrast it with surface level concept
drift; (2) build predictive models to forecast short-term shifts in meaning
from previous meaning as well as from concept drift; and (3) visualize
short-term representation shift for example keywords to demonstrate the
practical use of our approach to discover and track meaning of newly emerging
terms in social media. We show that short-term representation shift can be
accurately predicted up to several weeks in advance. Our unique approach to
modeling and visualizing word representation shifts in social media can be used
to explore and characterize specific aspects of the streaming corpus during
crisis events and potentially improve other downstream classification tasks
including real-time event detection
Heuristics Miners for Streaming Event Data
More and more business activities are performed using information systems.
These systems produce such huge amounts of event data that existing systems are
unable to store and process them. Moreover, few processes are in steady-state
and due to changing circumstances processes evolve and systems need to adapt
continuously. Since conventional process discovery algorithms have been defined
for batch processing, it is difficult to apply them in such evolving
environments. Existing algorithms cannot cope with streaming event data and
tend to generate unreliable and obsolete results.
In this paper, we discuss the peculiarities of dealing with streaming event
data in the context of process mining. Subsequently, we present a general
framework for defining process mining algorithms in settings where it is
impossible to store all events over an extended period or where processes
evolve while being analyzed. We show how the Heuristics Miner, one of the most
effective process discovery algorithms for practical applications, can be
modified using this framework. Different stream-aware versions of the
Heuristics Miner are defined and implemented in ProM. Moreover, experimental
results on artificial and real logs are reported
Session 4: Evolutionary Indeterminism
Proceedings of the Pittsburgh Workshop in History and Philosophy of Biology, Center for Philosophy of Science, University of Pittsburgh, March 23-24 2001 Session 4: Evolutionary Indeterminis
Incremental Predictive Process Monitoring: How to Deal with the Variability of Real Environments
A characteristic of existing predictive process monitoring techniques is to
first construct a predictive model based on past process executions, and then
use it to predict the future of new ongoing cases, without the possibility of
updating it with new cases when they complete their execution. This can make
predictive process monitoring too rigid to deal with the variability of
processes working in real environments that continuously evolve and/or exhibit
new variant behaviors over time. As a solution to this problem, we propose the
use of algorithms that allow the incremental construction of the predictive
model. These incremental learning algorithms update the model whenever new
cases become available so that the predictive model evolves over time to fit
the current circumstances. The algorithms have been implemented using different
case encoding strategies and evaluated on a number of real and synthetic
datasets. The results provide a first evidence of the potential of incremental
learning strategies for predicting process monitoring in real environments, and
of the impact of different case encoding strategies in this setting
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