Human age provides key demographic information. It is also considered as an important soft biometric trait for human identification or search. Compared to other pattern recognition problems (e.g., object classification, scene categorization), estimation much more challenging since the difference between facial images with variations can be subtle and process of aging varies greatly among different individuals. In this work, we investigate deep learning techniques based on convolutional...

In mymargin this communication, we study the application of supervised descent method (SDM) for 2-D microwave imaging. SDM contains offline training and online prediction. stage, a data set is generated according to prior information. Then, average directions between fixed initial model models can be learned by iterative schemes. reconstruction achieved through iterations based on directions. This scheme offers new perspective incorporate information into inversion reduce computational...

Electromagnetic (EM) imaging is widely applied in sensing for security, biomedicine, geophysics, and various industries. It an ill-posed inverse problem whose solution usually computationally expensive. Machine learning (ML) techniques especially deep (DL) show potential fast accurate imaging. However, the high performance of purely data-driven approaches relies on constructing a training set that statistically consistent with practical scenarios, which often not possible EM-imaging tasks....

Advanced driver assistance systems (ADAS) can be significantly improved with effective action prediction (DAP). Predicting actions early and accurately help mitigate the effects of potentially unsafe driving behaviors avoid possible accidents. In this paper, we formulate as a timeseries anomaly problem. While (driver interest) detection might trivial in context, finding patterns that consistently precede an requires searching for or extracting features across multi-modal sensory inputs. We...

Unsupervised learning for geometric perception (depth, optical flow, etc.) is of great interest to autonomous systems. Recent works on unsupervised have made considerable progress perceiving geometry; however, they usually ignore the coherence objects and perform poorly under scenarios with dark noisy environments. In contrast, supervised algorithms, which are robust, require large labeled dataset. This paper introduces SIGNet, a novel framework that provides robust geometry without...

Deep learning is applied to assist the joint inversion for audio-magnetotelluric and seismic travel time data. More specifically, deep residual convolutional neural networks (DRCNNs) are designed learn both structural similarity resistivity-velocity relationships according prior knowledge. During inversion, unknown resistivity velocity updated alternatingly with Gauss-Newton method, based on reference model generated by trained DRCNNs. The workflow of this scheme design DRCNNs explained in...

In this work, we design an iterative deep neural network to solve full-wave inverse scattering problems (ISPs) in the 2-D case. Forward modeling networks that predict scattered field are embedded inversion network. manner, predicts model update from residual between simulated data and observed data. The proposed can achieve super-resolution reconstruction meanwhile keeping of reconstructed models well consistent with We validate method both synthetic experimental inversion. Results show high...

In this study, physics-informed supervised residual learning (PhiSRL) is proposed to enable an effective, robust, and general deep framework for 2-D electromagnetic (EM) modeling. Based on the mathematical connection between fixed-point iteration method neural network (ResNet), PhiSRL aims solve a system of linear matrix equations. It applies convolutional networks (CNNs) learn updates solution with respect residuals. Inspired by stationary nonstationary iterative scheme method, ResNets...

Global oil and gas resources are declining continuously, sustainable development has become a common challenge worldwide. In terms of environmental protection economic benefits, the application microemulsions for enhanced recovery often requires fewer chemical agents, showing distinct advantages. This paper analyzes prospects trends middle-phase in tertiary recovery. The properties introduced, an overview historical development, theoretical framework, influencing factors, preparation methods...

In this article, a new scheme based on the supervised descent method (SDM) for solving directional electromagnetic logging-while-drilling (LWD) inverse problems is proposed. The SDM provides us perspective to combine classical gradient-based inversion and machine-learning-based schemes. It iteratively learns set of directions in offline training process, where model generated advance according prior information, then updates models with learned as well data residuals prediction stage,...

The supervised descent method (SDM) is applied to 2D magnetotellurics (MT) data inversion. SDM contains offline training and online prediction. set composed of the models generated according prior knowledge simulated by MT forward modeling. In process, a directions from an initial model learned. prediction, reconstruction achieved optimizing regularized objective function with restart scheme, where learned computed residual are involved. inversion has advantages (1) being more efficient than...

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> The absolute image reconstruction problem of electrical impedance tomography (EIT) is ill-posed. Traditional methods usually solve a nonlinear least squares with some kind regularization. These suffer from low accuracy, poor anti-noise performance, and long computation time. Besides, the integration xmlns:xlink="http://www.w3.org/1999/xlink">a priori information</i> not very...

In this work, we present a deep-learning-based low-frequency (LF) data prediction scheme to solve the highly nonlinear inverse scattering problem (ISP) with strong scatterers. The nonlinearity of ISP is alleviated by introducing LF components in full-wave inversion. scheme, deep neural network (DNN) trained predict absent scattered field from measured high-frequency (HF) data. Then, frequency-hopping technique applied invert predicted and HF data, where inverted model served as an initial...

This work proposes a novel deep learning (DL) framework to solve the electromagnetic inverse scattering (EMIS) problems. The proposed integrates complex-valued convolutional neural network (DConvNet) into supervised descent method (SDM) realize both off-line training and on-line "imaging" prediction for EMIS. offline consists of two parts: 1) DConvNet training: dataset is created, trained EM forward process 2) SDM integrated framework, average directions between initial true label iterative...

Polymer flooding is a critical enhanced oil recovery technique; however, the development of polymer channeling along dominant channels during its later stages can adversely affect process by increasing comprehensive water cut and dispersing remaining oil, thereby diminishing benefits. This study aims to address this challenge investigating identification methods distribution patterns in inform optimize strategy. Through series experiments, we analyzed how factors such as permeability,...

Abstract In geophysics, Bayesian inversion methods are of significant prominence. Here, we present a novel approach utilizing the Hamiltonian Monte Carlo (HMC) method in gravity for elucidating three‐dimensional (3D) density structures. HMC provides multi‐dimensional sampling that demonstrates enhanced optimization efficiency, facilitating attainment distant proposals with elevated acceptance probabilities. Its applicability also extends to resolving linear inverse problems. Three synthetic...

<title>Abstract</title> <bold>Background: </bold>The relationship between dietary oxidative balance and mortality among cancer patients remains unclear, particularly concerning the moderating effects of depression cancer-specific mortality. This study aimed to evaluate associations Dietary Oxidative Balance Score (DOBS) Circulating Antioxidants Index (CAI) with risk in patients, emphasizing influence status. <bold>Methods: </bold>Data were derived from two National Health Nutrition...

Facial expression recognition (FER) is an active research topic in computer vision. However, there no study yet to discover whether FER affected by human aging, from a computational perspective. We perform of within and across age groups compare the accuracies. Two databases psychology society are introduced vision community used for our study. found that influenced significantly we analyze influence interpret it viewpoint. Next, propose some schemes reduce aging on evaluate effectiveness...