12,022 research outputs found
Hierarchical information clustering by means of topologically embedded graphs
We introduce a graph-theoretic approach to extract clusters and hierarchies
in complex data-sets in an unsupervised and deterministic manner, without the
use of any prior information. This is achieved by building topologically
embedded networks containing the subset of most significant links and analyzing
the network structure. For a planar embedding, this method provides both the
intra-cluster hierarchy, which describes the way clusters are composed, and the
inter-cluster hierarchy which describes how clusters gather together. We
discuss performance, robustness and reliability of this method by first
investigating several artificial data-sets, finding that it can outperform
significantly other established approaches. Then we show that our method can
successfully differentiate meaningful clusters and hierarchies in a variety of
real data-sets. In particular, we find that the application to gene expression
patterns of lymphoma samples uncovers biologically significant groups of genes
which play key-roles in diagnosis, prognosis and treatment of some of the most
relevant human lymphoid malignancies
Closed-loop separation control over a sharp edge ramp using Genetic Programming
We experimentally perform open and closed-loop control of a separating
turbulent boundary layer downstream from a sharp edge ramp. The turbulent
boundary layer just above the separation point has a Reynolds number
based on momentum thickness. The goal of the
control is to mitigate separation and early re-attachment. The forcing employs
a spanwise array of active vortex generators. The flow state is monitored with
skin-friction sensors downstream of the actuators. The feedback control law is
obtained using model-free genetic programming control (GPC) (Gautier et al.
2015). The resulting flow is assessed using the momentum coefficient, pressure
distribution and skin friction over the ramp and stereo PIV. The PIV yields
vector field statistics, e.g. shear layer growth, the backflow area and vortex
region. GPC is benchmarked against the best periodic forcing. While open-loop
control achieves separation reduction by locking-on the shedding mode, GPC
gives rise to similar benefits by accelerating the shear layer growth.
Moreover, GPC uses less actuation energy.Comment: 24 pages, 24 figures, submitted to Experiments in Fluid
Percolation Analysis of a Wiener Reconstruction of the IRAS 1.2 Jy Redshift Catalog
We present percolation analyses of Wiener Reconstructions of the IRAS 1.2 Jy
Redshift Survey. There are ten reconstructions of galaxy density fields in real
space spanning the range to , where
, is the present dimensionless density and
is the bias factor. Our method uses the growth of the largest cluster
statistic to characterize the topology of a density field, where Gaussian
randomized versions of the reconstructions are used as standards for analysis.
For the reconstruction volume of radius, Mpc,
percolation analysis reveals a slight `meatball' topology for the real space,
galaxy distribution of the IRAS survey.
cosmology-galaxies:clustering-methods:numericalComment: Revised version accepted for publication in The Astrophysical
Journal, January 10, 1997 issue, Vol.47
Persistent Homology Guided Force-Directed Graph Layouts
Graphs are commonly used to encode relationships among entities, yet their
abstractness makes them difficult to analyze. Node-link diagrams are popular
for drawing graphs, and force-directed layouts provide a flexible method for
node arrangements that use local relationships in an attempt to reveal the
global shape of the graph. However, clutter and overlap of unrelated structures
can lead to confusing graph visualizations. This paper leverages the persistent
homology features of an undirected graph as derived information for interactive
manipulation of force-directed layouts. We first discuss how to efficiently
extract 0-dimensional persistent homology features from both weighted and
unweighted undirected graphs. We then introduce the interactive persistence
barcode used to manipulate the force-directed graph layout. In particular, the
user adds and removes contracting and repulsing forces generated by the
persistent homology features, eventually selecting the set of persistent
homology features that most improve the layout. Finally, we demonstrate the
utility of our approach across a variety of synthetic and real datasets
Analysis of Hardware Accelerated Deep Learning and the Effects of Degradation on Performance
As convolutional neural networks become more prevalent in research and real world applications, the need for them to be faster and more robust will be a constant battle. This thesis investigates the effect of degradation being introduced to an image prior to object recognition with a convolutional neural network. As well as experimenting with methods to reduce the degradation and improve performance. Gaussian smoothing and additive Gaussian noise are both analyzed degradation models within this thesis and are reduced with Gaussian and Butterworth masks using unsharp masking and smoothing, respectively. The results show that each degradation is disruptive to the performance of YOLOv3, with Gaussian smoothing producing a mean average precision of less than 20% and Gaussian noise producing a mean average precision as low as 0%. Reduction methods applied to the data give results of 1%-21% mean average precision increase over the baseline, varying based on the degradation model. These methods are also applied to an 8-bit quantized implementation of YOLOv3, which is intended to run on a Xilinx ZCU104 FPGA, which showed to be as robust as the oating point network, with results within 2% mean average precision of the oating point network. With the ZCU104 being able to process images of 416x416 at 25 frames per second which is comparable to a NVIDIA 2080 RTX, FPGAs are a viable solution to computing object detection on the edge. In conclusion, this thesis shows that degradation causes performance of a convolutional neural network (quantized and oating point) to lose accuracy to a level that the network is unable to accurately predict objects. However, the degradation can be reduced, and in most cases can elevate the performance of the network by using computer vision techniques to reduce the noise within the image
Network polarization, filter bubbles, and echo chambers: An annotated review of measures and reduction methods
Polarization arises when the underlying network connecting the members of a
community or society becomes characterized by highly connected groups with weak
inter-group connectivity. The increasing polarization, the strengthening of
echo chambers, and the isolation caused by information filters in social
networks are increasingly attracting the attention of researchers from
different areas of knowledge such as computer science, economics, social and
political sciences. This work presents an annotated review of network
polarization measures and models used to handle the polarization. Several
approaches for measuring polarization in graphs and networks were identified,
including those based on homophily, modularity, random walks, and balance
theory. The strategies used for reducing polarization include methods that
propose edge or node editions (including insertions or deletions, as well as
edge weight modifications), changes in social network design, or changes in the
recommendation systems embedded in these networks.Comment: Corrected a typo in Section 3.2; the rest remains unchange
Doctor of Philosophy
dissertationComputer programs have complex interactions with their underlying hardware, exhibiting complex behaviors as a result. It is critical to understand these programs, as they serve an importantrole: researchers use them to express new ideas in computer science, while many others derive production value from them. In both cases, program understanding leads to mastery over these functions, adding value to human endeavors. Memory behavior is one of the hallmarks of general program behavior: it represents the critical function of retrieving data for the program to work on; it often reflects the overall actions taken by the program, providing a signature of program behavior; and it is often an important performance bottleneck, as the the memory subsystem is typically much slower than the processor. These reasons justify an investigation into the memory behavior of programs. A memory reference trace is a list of memory transactions performed by a program at runtime, a rich data source capturing the whole of a program's interaction with the memory subsystem, and a clear starting point for investigating program memory behavior. However, such a trace is extremely difficult to interpret by mere inspection, as it consists solely of many, many addresses and operation codes, without any more structure or context. This dissertation proposes to use visualization to construct images and animations of the data within a reference trace, thereby visually transmitting structures and events as encoded in the trace. These visualization approaches are designed with different focuses, meant to expose various aspects of the trace. For instance, the time dimension of the reference traces can be handled either with animation, showing events as they occur, or by laying time out in a spatial dimension, giving a view of the entire history of the trace at once. The approaches also vary in their level of abstraction from the hardware: some are concretely connected to representations of the memory itself, while others are more free-form, using more abstract metaphors to highlight general behaviors and patterns, which in turn characterize the program behavior. Each approach delivers its own set of insights, as demonstrated in this dissertation
Effectiveness of dismantling strategies on moderated vs. unmoderated online social platforms
Online social networks are the perfect test bed to better understand
large-scale human behavior in interacting contexts. Although they are broadly
used and studied, little is known about how their terms of service and posting
rules affect the way users interact and information spreads. Acknowledging the
relation between network connectivity and functionality, we compare the
robustness of two different online social platforms, Twitter and Gab, with
respect to dismantling strategies based on the recursive censor of users
characterized by social prominence (degree) or intensity of inflammatory
content (sentiment). We find that the moderated (Twitter) vs unmoderated (Gab)
character of the network is not a discriminating factor for intervention
effectiveness. We find, however, that more complex strategies based upon the
combination of topological and content features may be effective for network
dismantling. Our results provide useful indications to design better strategies
for countervailing the production and dissemination of anti-social content in
online social platforms
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