28,056 research outputs found
From Traditional to Modern : Domain Adaptation for Action Classification in Short Social Video Clips
Short internet video clips like vines present a significantly wild
distribution compared to traditional video datasets. In this paper, we focus on
the problem of unsupervised action classification in wild vines using
traditional labeled datasets. To this end, we use a data augmentation based
simple domain adaptation strategy. We utilise semantic word2vec space as a
common subspace to embed video features from both, labeled source domain and
unlablled target domain. Our method incrementally augments the labeled source
with target samples and iteratively modifies the embedding function to bring
the source and target distributions together. Additionally, we utilise a
multi-modal representation that incorporates noisy semantic information
available in form of hash-tags. We show the effectiveness of this simple
adaptation technique on a test set of vines and achieve notable improvements in
performance.Comment: 9 pages, GCPR, 201
Bayesian learning of joint distributions of objects
There is increasing interest in broad application areas in defining flexible
joint models for data having a variety of measurement scales, while also
allowing data of complex types, such as functions, images and documents. We
consider a general framework for nonparametric Bayes joint modeling through
mixture models that incorporate dependence across data types through a joint
mixing measure. The mixing measure is assigned a novel infinite tensor
factorization (ITF) prior that allows flexible dependence in cluster allocation
across data types. The ITF prior is formulated as a tensor product of
stick-breaking processes. Focusing on a convenient special case corresponding
to a Parafac factorization, we provide basic theory justifying the flexibility
of the proposed prior and resulting asymptotic properties. Focusing on ITF
mixtures of product kernels, we develop a new Gibbs sampling algorithm for
routine implementation relying on slice sampling. The methods are compared with
alternative joint mixture models based on Dirichlet processes and related
approaches through simulations and real data applications.Comment: Appearing in Proceedings of the 16th International Conference on
Artificial Intelligence and Statistics (AISTATS) 2013, Scottsdale, AZ, US
Reconstructing the Traffic State by Fusion of Heterogeneous Data
We present an advanced interpolation method for estimating smooth
spatiotemporal profiles for local highway traffic variables such as flow, speed
and density. The method is based on stationary detector data as typically
collected by traffic control centres, and may be augmented by floating car data
or other traffic information. The resulting profiles display transitions
between free and congested traffic in great detail, as well as fine structures
such as stop-and-go waves. We establish the accuracy and robustness of the
method and demonstrate three potential applications: 1. compensation for gaps
in data caused by detector failure; 2. separation of noise from dynamic traffic
information; and 3. the fusion of floating car data with stationary detector
data.Comment: For more information see http://www.mtreiber.de or
http://www.akesting.d
Multi-Objective Gust Load Alleviation Control Designs for an Aeroelastic Wind Tunnel Demonstration Wing
This paper presents several control and gust disturbance estimation techniques applied to a mathematical model of a physical flexible wing wind tunnel model used in ongoing tests at the University of Washington Aeronautical Laboratory's Kirsten Wind Tunnel. Three methods of gust disturbance estimation are presented, followed by three control methods: LQG, Basic Multi-Objective (BMO), and a novel Multi-Objective Prediction Correction (MOPC) controller. The latter of which augments a multi-objective controller, and attempts to correct for errors in the disturbance estimate. A simplified linear simulation of the three controllers is performed and a simple MIMO stability and robustness assessment is performed. Then, the same controllers are simulated in a higher fidelity Simulink environment that captures sampling, saturation and noise effects. This preliminary analysis indicates that the BMO controller provides the best performance and largest stability margins
Information Fusion for Anomaly Detection with the Dendritic Cell Algorithm
Dendritic cells are antigen presenting cells that provide a vital link
between the innate and adaptive immune system, providing the initial detection
of pathogenic invaders. Research into this family of cells has revealed that
they perform information fusion which directs immune responses. We have derived
a Dendritic Cell Algorithm based on the functionality of these cells, by
modelling the biological signals and differentiation pathways to build a
control mechanism for an artificial immune system. We present algorithmic
details in addition to experimental results, when the algorithm was applied to
anomaly detection for the detection of port scans. The results show the
Dendritic Cell Algorithm is sucessful at detecting port scans.Comment: 21 pages, 17 figures, Information Fusio
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