2,101 research outputs found
Quantized Feedback Control of Network Empowerment Ammunition with Data-Rate Limitations
This paper investigates quantized feedback control problems for network empowerment ammunition, where the sensors and the controller are connected by a digital communication network with data-rate limitations. Different from the existing ones, a new bit-allocation algorithm on the basis of the singular values of the plant matrix is proposed to encode the plant states. A lower bound on the data rate is presented to ensure stabilization of the unstable plant. It is shown in our results that, the algorithm can be employed for the more general case. An illustrative example is given to demonstrate the effectiveness of the proposed algorithm
LIFE: Learning Individual Features for Multivariate Time Series Prediction with Missing Values
Multivariate time series (MTS) prediction is ubiquitous in real-world fields,
but MTS data often contains missing values. In recent years, there has been an
increasing interest in using end-to-end models to handle MTS with missing
values. To generate features for prediction, existing methods either merge all
input dimensions of MTS or tackle each input dimension independently. However,
both approaches are hard to perform well because the former usually produce
many unreliable features and the latter lacks correlated information. In this
paper, we propose a Learning Individual Features (LIFE) framework, which
provides a new paradigm for MTS prediction with missing values. LIFE generates
reliable features for prediction by using the correlated dimensions as
auxiliary information and suppressing the interference from uncorrelated
dimensions with missing values. Experiments on three real-world data sets
verify the superiority of LIFE to existing state-of-the-art models
Learning Sparse Representations for Fruit Fly Gene Expression Pattern Image Annotation and Retreival
Background: Fruit fly embryogenesis is one of the best understood animal development systems, and the spatiotemporal gene expression dynamics in this process are captured by digital images. Analysis of these high-throughput images will provide novel insights into the functions, interactions, and networks of animal genes governing development. To facilitate comparative analysis, web-based interfaces have been developed to conduct image retrieval based on body part keywords and images. Currently, the keyword annotation of spatiotemporal gene expression patterns is conducted manually. However, this manual practice does not scale with the continuously expanding collection of images. In addition, existing image retrieval systems based on the expression patterns may be made more accurate using keywords.
Results: In this article, we adapt advanced data mining and computer vision techniques to address the key challenges in annotating and retrieving fruit fly gene expression pattern images. To boost the performance of image annotation and retrieval, we propose representations integrating spatial information and sparse features, overcoming the limitations of prior schemes.
Conclusions: We perform systematic experimental studies to evaluate the proposed schemes in comparison with current methods. Experimental results indicate that the integration of spatial information and sparse features lead to consistent performance improvement in image annotation, while for the task of retrieval, sparse features alone yields better results
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