1,778 research outputs found
Exploration of User Groups in VEXUS
We introduce VEXUS, an interactive visualization framework for exploring user
data to fulfill tasks such as finding a set of experts, forming discussion
groups and analyzing collective behaviors. User data is characterized by a
combination of demographics like age and occupation, and actions such as rating
a movie, writing a paper, following a medical treatment or buying groceries.
The ubiquity of user data requires tools that help explorers, be they
specialists or novice users, acquire new insights. VEXUS lets explorers
interact with user data via visual primitives and builds an exploration profile
to recommend the next exploration steps. VEXUS combines state-of-the-art
visualization techniques with appropriate indexing of user data to provide fast
and relevant exploration
Multilevel Solvers for Unstructured Surface Meshes
Parameterization of unstructured surface meshes is of fundamental importance in many applications of digital geometry processing. Such parameterization approaches give rise to large and exceedingly ill-conditioned systems which are difficult or impossible to solve without the use of sophisticated multilevel preconditioning strategies. Since the underlying meshes are very fine to begin with, such multilevel preconditioners require mesh coarsening to build an appropriate hierarchy. In this paper we consider several strategies for the construction of hierarchies using ideas from mesh simplification algorithms used in the computer graphics literature. We introduce two novel hierarchy construction schemes and demonstrate their superior performance when used in conjunction with a multigrid preconditioner
Sparse Allreduce: Efficient Scalable Communication for Power-Law Data
Many large datasets exhibit power-law statistics: The web graph, social
networks, text data, click through data etc. Their adjacency graphs are termed
natural graphs, and are known to be difficult to partition. As a consequence
most distributed algorithms on these graphs are communication intensive. Many
algorithms on natural graphs involve an Allreduce: a sum or average of
partitioned data which is then shared back to the cluster nodes. Examples
include PageRank, spectral partitioning, and many machine learning algorithms
including regression, factor (topic) models, and clustering. In this paper we
describe an efficient and scalable Allreduce primitive for power-law data. We
point out scaling problems with existing butterfly and round-robin networks for
Sparse Allreduce, and show that a hybrid approach improves on both.
Furthermore, we show that Sparse Allreduce stages should be nested instead of
cascaded (as in the dense case). And that the optimum throughput Allreduce
network should be a butterfly of heterogeneous degree where degree decreases
with depth into the network. Finally, a simple replication scheme is introduced
to deal with node failures. We present experiments showing significant
improvements over existing systems such as PowerGraph and Hadoop
A Contextual-Bandit Approach to Personalized News Article Recommendation
Personalized web services strive to adapt their services (advertisements,
news articles, etc) to individual users by making use of both content and user
information. Despite a few recent advances, this problem remains challenging
for at least two reasons. First, web service is featured with dynamically
changing pools of content, rendering traditional collaborative filtering
methods inapplicable. Second, the scale of most web services of practical
interest calls for solutions that are both fast in learning and computation.
In this work, we model personalized recommendation of news articles as a
contextual bandit problem, a principled approach in which a learning algorithm
sequentially selects articles to serve users based on contextual information
about the users and articles, while simultaneously adapting its
article-selection strategy based on user-click feedback to maximize total user
clicks.
The contributions of this work are three-fold. First, we propose a new,
general contextual bandit algorithm that is computationally efficient and well
motivated from learning theory. Second, we argue that any bandit algorithm can
be reliably evaluated offline using previously recorded random traffic.
Finally, using this offline evaluation method, we successfully applied our new
algorithm to a Yahoo! Front Page Today Module dataset containing over 33
million events. Results showed a 12.5% click lift compared to a standard
context-free bandit algorithm, and the advantage becomes even greater when data
gets more scarce.Comment: 10 pages, 5 figure
On Submodularity and Controllability in Complex Dynamical Networks
Controllability and observability have long been recognized as fundamental
structural properties of dynamical systems, but have recently seen renewed
interest in the context of large, complex networks of dynamical systems. A
basic problem is sensor and actuator placement: choose a subset from a finite
set of possible placements to optimize some real-valued controllability and
observability metrics of the network. Surprisingly little is known about the
structure of such combinatorial optimization problems. In this paper, we show
that several important classes of metrics based on the controllability and
observability Gramians have a strong structural property that allows for either
efficient global optimization or an approximation guarantee by using a simple
greedy heuristic for their maximization. In particular, the mapping from
possible placements to several scalar functions of the associated Gramian is
either a modular or submodular set function. The results are illustrated on
randomly generated systems and on a problem of power electronic actuator
placement in a model of the European power grid.Comment: Original arXiv version of IEEE Transactions on Control of Network
Systems paper (Volume 3, Issue 1), with a addendum (located in the ancillary
documents) that explains an error in a proof of the original paper and
provides a counterexample to the corresponding resul
Book of Abstracts of the Sixth SIAM Workshop on Combinatorial Scientific Computing
Book of Abstracts of CSC14 edited by Bora UçarInternational audienceThe Sixth SIAM Workshop on Combinatorial Scientific Computing, CSC14, was organized at the Ecole Normale Supérieure de Lyon, France on 21st to 23rd July, 2014. This two and a half day event marked the sixth in a series that started ten years ago in San Francisco, USA. The CSC14 Workshop's focus was on combinatorial mathematics and algorithms in high performance computing, broadly interpreted. The workshop featured three invited talks, 27 contributed talks and eight poster presentations. All three invited talks were focused on two interesting fields of research specifically: randomized algorithms for numerical linear algebra and network analysis. The contributed talks and the posters targeted modeling, analysis, bisection, clustering, and partitioning of graphs, applied in the context of networks, sparse matrix factorizations, iterative solvers, fast multi-pole methods, automatic differentiation, high-performance computing, and linear programming. The workshop was held at the premises of the LIP laboratory of ENS Lyon and was generously supported by the LABEX MILYON (ANR-10-LABX-0070, Université de Lyon, within the program ''Investissements d'Avenir'' ANR-11-IDEX-0007 operated by the French National Research Agency), and by SIAM
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