11,723 research outputs found
A stigmergy-based analysis of city hotspots to discover trends and anomalies in urban transportation usage
A key aspect of a sustainable urban transportation system is the
effectiveness of transportation policies. To be effective, a policy has to
consider a broad range of elements, such as pollution emission, traffic flow,
and human mobility. Due to the complexity and variability of these elements in
the urban area, to produce effective policies remains a very challenging task.
With the introduction of the smart city paradigm, a widely available amount of
data can be generated in the urban spaces. Such data can be a fundamental
source of knowledge to improve policies because they can reflect the
sustainability issues underlying the city. In this context, we propose an
approach to exploit urban positioning data based on stigmergy, a bio-inspired
mechanism providing scalar and temporal aggregation of samples. By employing
stigmergy, samples in proximity with each other are aggregated into a
functional structure called trail. The trail summarizes relevant dynamics in
data and allows matching them, providing a measure of their similarity.
Moreover, this mechanism can be specialized to unfold specific dynamics.
Specifically, we identify high-density urban areas (i.e hotspots), analyze
their activity over time, and unfold anomalies. Moreover, by matching activity
patterns, a continuous measure of the dissimilarity with respect to the typical
activity pattern is provided. This measure can be used by policy makers to
evaluate the effect of policies and change them dynamically. As a case study,
we analyze taxi trip data gathered in Manhattan from 2013 to 2015.Comment: Preprin
Parameter estimation for many-particle models from aggregate observations: A Wasserstein distance based sequential Monte Carlo sampler
In this work we study systems consisting of a group of moving particles. In
such systems, often some important parameters are unknown and have to be
estimated from observed data. Such parameter estimation problems can often be
solved via a Bayesian inference framework. However in many practical problems,
only data at the aggregate level is available and as a result the likelihood
function is not available, which poses challenge for Bayesian methods. In
particular, we consider the situation where the distributions of the particles
are observed. We propose a Wasserstein distance based sequential Monte Carlo
sampler to solve the problem: the Wasserstein distance is used to measure the
similarity between the observed and the simulated particle distributions and
the sequential Monte Carlo samplers is used to deal with the sequentially
available observations. Two real-world examples are provided to demonstrate the
performance of the proposed method
Differentiated cell behavior: a multiscale approach using measure theory
This paper deals with the derivation of a collective model of cell
populations out of an individual-based description of the underlying physical
particle system. By looking at the spatial distribution of cells in terms of
time-evolving measures, rather than at individual cell paths, we obtain an
ensemble representation stemming from the phenomenological behavior of the
single component cells. In particular, as a key advantage of our approach, the
scale of representation of the system, i.e., microscopic/discrete vs.
macroscopic/continuous, can be chosen a posteriori according only to the
spatial structure given to the aforesaid measures. The paper focuses in
particular on the use of different scales based on the specific functions
performed by cells. A two-population hybrid system is considered, where cells
with a specialized/differentiated phenotype are treated as a discrete
population of point masses while unspecialized/undifferentiated cell aggregates
are represented with a continuous approximation. Numerical simulations and
analytical investigations emphasize the role of some biologically relevant
parameters in determining the specific evolution of such a hybrid cell system.Comment: 25 pages, 6 figure
Towards Accountable AI: Hybrid Human-Machine Analyses for Characterizing System Failure
As machine learning systems move from computer-science laboratories into the
open world, their accountability becomes a high priority problem.
Accountability requires deep understanding of system behavior and its failures.
Current evaluation methods such as single-score error metrics and confusion
matrices provide aggregate views of system performance that hide important
shortcomings. Understanding details about failures is important for identifying
pathways for refinement, communicating the reliability of systems in different
settings, and for specifying appropriate human oversight and engagement.
Characterization of failures and shortcomings is particularly complex for
systems composed of multiple machine learned components. For such systems,
existing evaluation methods have limited expressiveness in describing and
explaining the relationship among input content, the internal states of system
components, and final output quality. We present Pandora, a set of hybrid
human-machine methods and tools for describing and explaining system failures.
Pandora leverages both human and system-generated observations to summarize
conditions of system malfunction with respect to the input content and system
architecture. We share results of a case study with a machine learning pipeline
for image captioning that show how detailed performance views can be beneficial
for analysis and debugging
Online real-time crowd behavior detection in video sequences
Automatically detecting events in crowded scenes is a challenging task in Computer Vision. A number of offline approaches have been proposed for solving the problem of crowd behavior detection, however the offline assumption limits their application in real-world video surveillance systems. In this paper, we propose an online and real-time method for detecting events in crowded video sequences. The proposed approach is based on the combination of visual feature extraction and image segmentation and it works without the need of a training phase. A quantitative experimental evaluation has been carried out on multiple publicly available video sequences, containing data from various crowd scenarios and different types of events, to demonstrate the effectiveness of the approach
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