Noisy Dense Label Prediction with Noise-Robust Loss Functions

2022 ◽  
pp. 683-694
Author(s):  
Redwan Hasif Alvi ◽  
Rashedur M. Rahman
Keyword(s):  
Loss Functions ◽  
Label Prediction ◽  
Noise Robust ◽  
Dense Label

Immunocytochemistry. A Review of Methodology for Electron Microscopic Applicaiton

1974 ◽  
Vol 32 ◽  
pp. 328-329
Author(s):  
Joseph E. Mazurkiewicz
Keyword(s):  
Electron Microscopy ◽  
Light Microscopy ◽  
Ultrastructural Level ◽  
Electron Microscopic ◽  
Biological Interest ◽  
Investigative Approach ◽  
Immunologic Activity ◽  
Dense Label

Immunocytochemistry is a powerful investigative approach in which one of the most exacting examples of specificity, that of the reaction of an antibody with its antigen, isused to localize tissue and cell specific molecules in situ. Following the introduction of fluorescent labeled antibodies in T950, a large number of molecules of biological interest had been studied with light microscopy, especially antigens involved in the pathogenesis of some diseases. However, with advances in electron microscopy, newer methods were needed which could reveal these reactions at the ultrastructural level. An electron dense label that could be coupled to an antibody without the loss of immunologic activity was desired.

Valence electron spectroscopy of inhomogeneous media

1992 ◽  
Vol 50 (2) ◽  
pp. 1150-1151
Author(s):  
A. Howie ◽  
D.W. McComb
Keyword(s):  
Specific Gravity ◽  
Loss Function ◽  
Electron Spectroscopy ◽  
Valence Electron ◽  
Peak Height ◽  
Loss Functions ◽  
Loss Peak ◽  
High Energies ◽  
Valence Electron Density ◽  
Electron Microscopist

The bulk loss function Im(-l/ε (ω)), a well established tool for the interpretation of valence loss spectra, is being progressively adapted to the wide variety of inhomogeneous samples of interest to the electron microscopist. Proportionality between n, the local valence electron density, and ε-1 (Sellmeyer's equation) has sometimes been assumed but may not be valid even in homogeneous samples. Figs. 1 and 2 show the experimentally measured bulk loss functions for three pure silicates of different specific gravity ρ - quartz (ρ = 2.66), coesite (ρ = 2.93) and a zeolite (ρ = 1.79). Clearly, despite the substantial differences in density, the shift of the prominent loss peak is very small and far less than that predicted by scaling e for quartz with Sellmeyer's equation or even the somewhat smaller shift given by the Clausius-Mossotti (CM) relation which assumes proportionality between n (or ρ in this case) and (ε - 1)/(ε + 2). Both theories overestimate the rise in the peak height for coesite and underestimate the increase at high energies.

Integrated Ambient Light Sensor with an LTPS Noise-Robust Circuit and a-Si Photodiodes for AMLCDs

2010 ◽  
Vol E93-C (11) ◽  
pp. 1583-1589
Author(s):  
Fumirou MATSUKI ◽  
Kazuyuki HASHIMOTO ◽  
Keiichi SANO ◽  
Fu-Yuan HSUEH ◽  
Ramesh KAKKAD ◽  
...  
Keyword(s):  
Ambient Light ◽  
Light Sensor ◽  
Noise Robust

Noise Robust Acoustic to Articulatory Speech Inversion

2018 ◽  
Author(s):  
Nadee Seneviratne ◽  
Ganesh Sivaraman ◽  
Vikramjit Mitra ◽  
Carol Espy-Wilson
Keyword(s):  
Noise Robust

Uncertainty Decoding with Adaptive Sampling for Noise Robust DNN-Based Acoustic Modeling

2017 ◽  
Author(s):  
Dung T. Tran ◽  
Marc Delcroix ◽  
Atsunori Ogawa ◽  
Tomohiro Nakatani
Keyword(s):  
Adaptive Sampling ◽  
Acoustic Modeling ◽  
Noise Robust

A Deep Identity Representation for Noise Robust Spoofing Detection

2018 ◽  
Author(s):  
Alejandro Gómez Alanís ◽  
Antonio M. Peinado ◽  
Jose A. Gonzalez ◽  
Angel Gomez
Keyword(s):  
Spoofing Detection ◽  
Identity Representation ◽  
Noise Robust

A CLASS OF LORENZ CURVES BASED ON LINEAR EXPONENTIAL LOSS FUNCTIONS

2002 ◽  
Vol 31 (6) ◽  
pp. 925-942 ◽  
Author(s):  
José María Sarabia ◽  
Marta Pascual
Keyword(s):  
Loss Functions ◽  
Lorenz Curves

scholarly journals Study on Radar Echo-Filling in an Occlusion Area by a Deep Learning Algorithm

2021 ◽  
Vol 13 (9) ◽  
pp. 1779
Author(s):  
Xiaoyan Yin ◽  
Zhiqun Hu ◽  
Jiafeng Zheng ◽  
Boyong Li ◽  
Yuanyuan Zuo
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Learning Algorithm ◽  
Weather Radar ◽  
Loss Functions ◽  
Training Dataset ◽  
Echo Intensity ◽  
Common Mean ◽  
Deep Learning Algorithm ◽  
Radar Beam

Radar beam blockage is an important error source that affects the quality of weather radar data. An echo-filling network (EFnet) is proposed based on a deep learning algorithm to correct the echo intensity under the occlusion area in the Nanjing S-band new-generation weather radar (CINRAD/SA). The training dataset is constructed by the labels, which are the echo intensity at the 0.5° elevation in the unblocked area, and by the input features, which are the intensity in the cube including multiple elevations and gates corresponding to the location of bottom labels. Two loss functions are applied to compile the network: one is the common mean square error (MSE), and the other is a self-defined loss function that increases the weight of strong echoes. Considering that the radar beam broadens with distance and height, the 0.5° elevation scan is divided into six range bands every 25 km to train different models. The models are evaluated by three indicators: explained variance (EVar), mean absolute error (MAE), and correlation coefficient (CC). Two cases are demonstrated to compare the effect of the echo-filling model by different loss functions. The results suggest that EFnet can effectively correct the echo reflectivity and improve the data quality in the occlusion area, and there are better results for strong echoes when the self-defined loss function is used.

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