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In this paper, we present a polarimetric image restoration approach that aims to recover the Stokes parameters and the degree of linear polarization from their corresponding degraded counterparts. The Stokes parameters and the degree of linear polarization are affected due to the degradations present in partial occlusion or turbid media, such as scattering, attenuation, and turbid water. The polarimetric image restoration with corresponding Mueller matrix estimation is performed using polarization-informed deep learning and 3D Integral imaging. An unsupervised image-to-image translation (UNIT) framework is utilized to obtain clean Stokes parameters from the degraded ones. Additionally, a multi-output convolutional neural network (CNN) based branch is used to predict the Mueller matrix estimate along with an estimate of the corresponding residue. The degree of linear polarization with the Mueller matrix estimate generates information regarding the characteristics of the underlying transmission media and the object under consideration. The approach has been evaluated under different environmentally degraded conditions, such as various levels of turbidity and partial occlusion. The 3D integral imaging reduces the effects of degradations in a turbid medium. The performance comparison between 3D and 2D imaging in varying scene conditions is provided. Experimental results suggest that the proposed approach is promising under the scene degradations considered. To the best of our knowledge, this is the first report on polarization-informed deep learning in 3D imaging, which attempts to recover the polarimetric information along with the corresponding Mueller matrix estimate in a degraded environment. 

Deep learning has shown incredible potential across a wide array of tasks, and accompanied by this growth has been an insatiable appetite for data. However, a large amount of data needed for enabling deep learning is stored on personal devices, and recent concerns on privacy have further highlighted challenges for accessing such data. As a result, federated learning (FL) has emerged as an important privacy-preserving technology that enables collaborative training of machine learning models without the need to send the raw, potentially sensitive, data to a central server. However, the fundamental premise that sending model updates to a server is privacy-preserving only holds if the updates cannot be “reverse engineered” to infer information about the private training data. It has been shown under a wide variety of settings that this privacy premise doesnothold. In this article we provide a comprehensive literature review of the different privacy attacks and defense methods in FL. We identify the current limitations of these attacks and highlight the settings in which the privacy of an FL client can be broken. We further dissect some of the successful industry applications of FL and draw lessons for future successful adoption. We survey the emerging landscape of privacy regulation for FL and conclude with future directions for taking FL toward the cherished goal of generating accurate models while preserving the privacy of the data from its participants. 

The problem of maximizing the adoption of a product through viral marketing in social networks has been studied heavily through postulated network models. We present a novel data-driven formulation of the problem. We use Graph Neural Networks (GNNs) to model the adoption of products by utilizing both topological and attribute information. The resulting Dynamic Viral Marketing (DVM) problem seeks to find the minimum budget and minimal set of dynamic topological and attribute changes in order to attain a specified adoption goal. We show that DVM is NP-Hard and is related to the existing influence maximization problem. Motivated by this connection, we develop the idea of Dynamic Gradient Influencing (DGI) that uses gradient ranking to find optimal perturbations and targets low-budget and high influence non-adopters in discrete steps. We use an efficient strategy for computing node budgets and develop the “Meta-Influence” heuristic for assessing a node’s downstream influence. We evaluate DGI against multiple baselines and demonstrate gains on average of 24% on budget and 37% on AUC on real world attributed networks. Our code is publicly available at https: //github.com/saurabhsharma1993/dynamic_viral_marketing.