scholarly journals Deep Compressed Sensing for Learning Submodular Functions

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2591 ◽  
Author(s):  
Yu-Chung Tsai ◽  
Kuo-Shih Tseng

The AI community has been paying attention to submodular functions due to their various applications (e.g., target search and 3D mapping). Learning submodular functions is a challenge since the number of a function’s outcomes of N sets is 2 N . The state-of-the-art approach is based on compressed sensing techniques, which are to learn submodular functions in the Fourier domain and then recover the submodular functions in the spatial domain. However, the number of Fourier bases is relevant to the number of sets’ sensing overlapping. To overcome this issue, this research proposed a submodular deep compressed sensing (SDCS) approach to learning submodular functions. The algorithm consists of learning autoencoder networks and Fourier coefficients. The learned networks can be applied to predict 2 N values of submodular functions. Experiments conducted with this approach demonstrate that the algorithm is more efficient than the benchmark approach.

2021 ◽  
Vol 23 (Supplement_E) ◽  
pp. E25-E27
Author(s):  
Stefano Bianchi ◽  
Filippo Maria Cauti

Abstract Nowadays, ablation of ventricular tachycardia (VT) in structural heart disease is an increasingly used procedure. In fact, it is the most effective strategy in controlling arrhythmic burden in VT patients. The ablative approaches are the result of the last 10 years of technological advances (Catheters, 3D mapping systems) and the constant study of the pathophysiological mechanisms underlying arrhythmic circuits. This presentation seeks to revisit the state of the art in the ablative treatment of VT.


2020 ◽  
Vol 9 (3) ◽  
pp. 668 ◽  
Author(s):  
Pietro Emanuele Napoli ◽  
Matteo Nioi ◽  
Lorenzo Mangoni ◽  
Pietro Gentile ◽  
Mirco Braghiroli ◽  
...  

In the last few decades, the ocular surface and the tear film have been noninvasively investigated in vivo, in a three-dimensional, high resolution, and real-time mode, by optical coherence tomography (OCT). Recently, OCT technology has made great strides in improving the acquisition speed and image resolution, thus increasing its impact in daily clinical practice and in the research setting. All these results have been achieved because of a transition from traditional time-domain (TD) to Fourier-domain (FD) technology. FD-OCT devices include a spectrometer in the receiver that analyzes the spectrum of reflected light on the retina or ocular surface and transforms it into information about the depth of the structures according to the Fourier principle. In this review, we summarize and provide the state-of-the-art in FD-OCT imaging of the ocular surface system, addressing specific aspects such as tear film dynamics and epithelial changes under physiologic and pathologic conditions. A theory on the dynamic nature of the tear film has been developed to explain the variations within the individual compartments. Moreover, an integrative model of tear film behavior during the inter-blink period and visual fixation is proposed.


Author(s):  
T. A. Welton

Various authors have emphasized the spatial information resident in an electron micrograph taken with adequately coherent radiation. In view of the completion of at least one such instrument, this opportunity is taken to summarize the state of the art of processing such micrographs. We use the usual symbols for the aberration coefficients, and supplement these with £ and 6 for the transverse coherence length and the fractional energy spread respectively. He also assume a weak, biologically interesting sample, with principal interest lying in the molecular skeleton remaining after obvious hydrogen loss and other radiation damage has occurred.


2003 ◽  
Vol 48 (6) ◽  
pp. 826-829 ◽  
Author(s):  
Eric Amsel
Keyword(s):  

1968 ◽  
Vol 13 (9) ◽  
pp. 479-480
Author(s):  
LEWIS PETRINOVICH
Keyword(s):  

1984 ◽  
Vol 29 (5) ◽  
pp. 426-428
Author(s):  
Anthony R. D'Augelli

1991 ◽  
Vol 36 (2) ◽  
pp. 140-140
Author(s):  
John A. Corson
Keyword(s):  

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