881 research outputs found
Wavelet frame bijectivity on Lebesgue and Hardy spaces
We prove a sufficient condition for frame-type wavelet series in , the
Hardy space , and BMO. For example, functions in these spaces are shown to
have expansions in terms of the Mexican hat wavelet, thus giving a strong
answer to an old question of Meyer.
Bijectivity of the wavelet frame operator acting on Hardy space is
established with the help of new frequency-domain estimates on the
Calder\'on-Zygmund constants of the frame kernel.Comment: 23 pages, 7 figure
Automatically learning structural units in educational videos with the hierarchical hidden Markov models
In this paper we present a coherent approach using the hierarchical HMM with shared structures to extract the structural units that form the building blocks of an education/training video. Rather than using hand-crafted approaches to define the structural units, we use the data from nine training videos to learn the parameters of the HHMM, and thus naturally extract the hierarchy. We then study this hierarchy and examine the nature of the structure at different levels of abstraction. Since the observable is continuous, we also show how to extend the parameter learning in the HHMM to deal with continuous observations
Microstructure and mechanical behavior of ultrafine-grained Ni processed by different powder metallurgy methods
Ultrafine-grained samples were produced from a Ni nanopowder by hot isostatic pressing
(HIP) and spark plasma sintering (SPS). The microstructure and mechanical behavior of
the two specimens were compared. The grain coarsening observed during the SPS
procedure was moderated due to a reduced temperature and time of consolidation
compared with HIP processing. The smaller grain-size and higher nickel-oxide content in
the SPS-processed sample resulted in a higher yield strength. Compression experiments
showed that the specimen produced by SPS reached a maximal flow stress at a small
strain, which was followed by a long steady-state softening while the HIP-processed
sample hardened until failure. It was revealed that the softening of the SPS-processed
sample resulted from microcracking along the grain boundaries
AdaBoost.MRF: boosted Markov random forests and application to multilevel activity recognition
Activity recognition is an important issue in building intelligent monitoring systems. We address the recognition of multilevel activities in this paper via a conditional Markov random field (MRF), known as the dynamic conditional random field (DCRF). Parameter estimation in general MRFs using maximum likelihood is known to be computationally challenging (except for extreme cases), and thus we propose an efficient boosting-based algorithm AdaBoost.MRF for this task. Distinct from most existing work, our algorithm can handle hidden variables (missing labels) and is particularly attractive for smarthouse domains where reliable labels are often sparsely observed. Furthermore, our method works exclusively on trees and thus is guaranteed to converge. We apply the AdaBoost.MRF algorithm to a home video surveillance application and demonstrate its efficacy
Accounting conservatism and banking expertise on board of directors
Previous studies show mixed evidence of the role of banking expertise on the board of directors on accounting conservatism. In this paper, we add to this growing literature by providing an innovative way to measure banking expertise based on life-time working history in banks of all individual directors on the board. We find that accounting conservatism is negatively affected by banking expertise on the board. Also, the results indicate that banking expertise on the board has a more pronounced impact on accounting conservatism when firms have high bankruptcy risk and when firms have high financial leverage. The evidence has some implications for boards of directors
Factored state-abstract hidden Markov models for activity recognition using pervasive multi-modal sensors
Current probabilistic models for activity recognition do not incorporate much sensory input data due to the problem of state space explosion. In this paper, we propose a model for activity recognition, called the Factored State-Abtract Hidden Markov Model (FS-AHMM) to allow us to integrate many sensors for improving recognition performance. The proposed FS-AHMM is an extension of the Abstract Hidden Markov Model which applies the concept of factored state representations to compactly represent the state transitions. The parameters of the FS-AHMM are estimated using the EM algorithm from the data acquired through multiple multi-modal sensors and cameras. The model is evaluated and compared with other existing models on real-world data. The results show that the proposed model outperforms other models and that the integrated sensor information helps in recognizing activity more accurately
Hierarchical semi-markov conditional random fields for recursive sequential data
Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of embedded undirected Markov chains to model complex hierarchical, nested Markov processes. It is parameterised in a discriminative framework and has polynomial time algorithms for learning and inference. Importantly, we develop efficient algorithms for learning and constrained inference in a partially-supervised setting, which is important issue in practice where labels can only be obtained sparsely. We demonstrate the HSCRF in two applications: (i) recognising human activities of daily living (ADLs) from indoor surveillance cameras, and (ii) noun-phrase chunking. We show that the HSCRF is capable of learning rich hierarchical models with reasonable accuracy in both fully and partially observed data cases.<br /
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