Ajit Rajwade
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View article: 3-Dimensional CryoEM Pose Estimation and Shift Correction Pipeline
3-Dimensional CryoEM Pose Estimation and Shift Correction Pipeline Open
Accurate pose estimation and shift correction are key challenges in cryo-EM due to the very low SNR, which directly impacts the fidelity of 3D reconstructions. We present an approach for pose estimation in cryo-EM that leverages multi-dime…
View article: Dictionary-Based Reconstruction of Spatio-Temporal 3D Magnetic Field Images from Quantum Diamond Microscope
Dictionary-Based Reconstruction of Spatio-Temporal 3D Magnetic Field Images from Quantum Diamond Microscope Open
Three-dimensional magnetic imaging with high spatio-temporal resolution is critical for probing current paths in various systems, from biosensing to microelectronics. Conventional 2D Fourier-based current source localization methods are il…
View article: Fast Debiasing of the LASSO Estimator
Fast Debiasing of the LASSO Estimator Open
In high-dimensional sparse regression, the \textsc{Lasso} estimator offers excellent theoretical guarantees but is well-known to produce biased estimates. To address this, \cite{Javanmard2014} introduced a method to ``debias" the \textsc{L…
View article: Two-Dimensional Unknown View Tomography from Unknown Angle Distributions
Two-Dimensional Unknown View Tomography from Unknown Angle Distributions Open
This study presents a technique for 2D tomography under unknown viewing angles when the distribution of the viewing angles is also unknown. Unknown view tomography (UVT) is a problem encountered in cryo-electron microscopy and in the geome…
View article: Robust Non-adaptive Group Testing under Errors in Group Membership Specifications
Robust Non-adaptive Group Testing under Errors in Group Membership Specifications Open
Given $p$ samples, each of which may or may not be defective, group testing (GT) aims to determine their defect status by performing tests on $n < p$ `groups', where a group is formed by mixing a subset of the $p$ samples. Assuming that th…
View article: Compressive Recovery of Signals Defined on Perturbed Graphs
Compressive Recovery of Signals Defined on Perturbed Graphs Open
Recovery of signals with elements defined on the nodes of a graph, from compressive measurements is an important problem, which can arise in various domains such as sensor networks, image reconstruction and group testing. In some scenarios…
View article: Group Testing for Accurate and Efficient Range-Based Near Neighbor Search for Plagiarism Detection
Group Testing for Accurate and Efficient Range-Based Near Neighbor Search for Plagiarism Detection Open
This work presents an adaptive group testing framework for the range-based high dimensional near neighbor search problem. Our method efficiently marks each item in a database as neighbor or non-neighbor of a query point, based on a cosine …
View article: Unlabelled Sensing with Priors: Algorithm and Bounds
Unlabelled Sensing with Priors: Algorithm and Bounds Open
In this study, we consider a variant of unlabelled sensing where the measurements are sparsely permuted, and additionally, a few correspondences are known. We present an estimator to solve for the unknown vector. We derive a theoretical up…
View article: Performance Bounds for LASSO under Multiplicative Noise: Applications to Pooled RT-PCR Testing
Performance Bounds for LASSO under Multiplicative Noise: Applications to Pooled RT-PCR Testing Open
Group testing is a technique which avoids individually testing $n$ samples for a rare disease and instead tests $n < p$ pools, where a pool consists of a mixture of small, equal portions of a subset of the $p$ samples. Group testing saves …
View article: Efficient Neural Network based Classification and Outlier Detection for Image Moderation using Compressed Sensing and Group Testing
Efficient Neural Network based Classification and Outlier Detection for Image Moderation using Compressed Sensing and Group Testing Open
Popular social media platforms employ neural network based image moderation engines to classify images uploaded on them as having potentially objectionable content. Such moderation engines must answer a large number of queries with heavy c…
View article: Estimating Joint Probability Distribution With Low-Rank Tensor Decomposition, Radon Transforms and Dictionaries
Estimating Joint Probability Distribution With Low-Rank Tensor Decomposition, Radon Transforms and Dictionaries Open
In this paper, we describe a method for estimating the joint probability density from data samples by assuming that the underlying distribution can be decomposed as a mixture of product densities with few mixture components. Prior works ha…
View article: Signal Reconstruction from Samples at Unknown Locations with Application to 2D Unknown View Tomography
Signal Reconstruction from Samples at Unknown Locations with Application to 2D Unknown View Tomography Open
It is well known that a band-limited signal can be reconstructed from its uniformly spaced samples if the sampling rate is sufficiently high. More recently, it has been proved that one can reconstruct a 1D band-limited signal even if the e…
View article: Group Testing with Side Information via Generalized Approximate Message Passing
Group Testing with Side Information via Generalized Approximate Message Passing Open
Group testing can help maintain a widespread testing program using fewer resources amid a pandemic. In a group testing setup, we are given n samples, one per individual. Each individual is either infected or uninfected. These samples are a…
View article: GlobalFlowNet: Video Stabilization using Deep Distilled Global Motion Estimates
GlobalFlowNet: Video Stabilization using Deep Distilled Global Motion Estimates Open
Videos shot by laymen using hand-held cameras contain undesirable shaky motion. Estimating the global motion between successive frames, in a manner not influenced by moving objects, is central to many video stabilization techniques, but po…
View article: Joint Probability Estimation Using Tensor Decomposition and Dictionaries
Joint Probability Estimation Using Tensor Decomposition and Dictionaries Open
In this work, we study non-parametric estimation of joint probabilities of a given set of discrete and continuous random variables from their (empirically estimated) 2D marginals, under the assumption that the joint probability could be de…
View article: A Weighted Generalized Coherence Approach for Sensing Matrix Design
A Weighted Generalized Coherence Approach for Sensing Matrix Design Open
As compared to using randomly generated sensing matrices, optimizing the sensing matrix w.r.t. a carefully designed criterion is known to lead to better quality signal recovery given a set of compressive measurements. In this paper, we pro…
View article: Contact Tracing Information Improves the Performance of Group Testing\n Algorithms
Contact Tracing Information Improves the Performance of Group Testing\n Algorithms Open
Group testing can help maintain a widespread testing program using fewer\nresources amid a pandemic. In group testing, we are given $n$ samples, one per\nindividual. These samples are arranged into $m < n$ pooled samples, where each\npool …
View article: Contact Tracing Information Improves the Performance of Group Testing Algorithms
Contact Tracing Information Improves the Performance of Group Testing Algorithms Open
Group testing can help maintain a widespread testing program using fewer resources amid a pandemic. In group testing, we are given $n$ samples, one per individual. These samples are arranged into $m < n$ pooled samples, where each pool is …
View article: Contact Tracing Enhances the Efficiency of Covid-19 Group Testing
Contact Tracing Enhances the Efficiency of Covid-19 Group Testing Open
Group testing can save testing resources in the context of the ongoing COVID-19 pandemic. In group testing, we are given $n$ samples, one per individual, and arrange them into $m < n$ pooled samples, where each pool is obtained by mixing a…
View article: Recovery of Joint Probability Distribution from one-way marginals: Low rank Tensors and Random Projections
Recovery of Joint Probability Distribution from one-way marginals: Low rank Tensors and Random Projections Open
Joint probability mass function (PMF) estimation is a fundamental machine learning problem. The number of free parameters scales exponentially with respect to the number of random variables. Hence, most work on nonparametric PMF estimation…
View article: Analyzing Cross Validation In Compressed Sensing With Mixed Gaussian And Impulse Measurement Noise With L1 Errors
Analyzing Cross Validation In Compressed Sensing With Mixed Gaussian And Impulse Measurement Noise With L1 Errors Open
Compressed sensing (CS) involves sampling signals at rates less than their Nyquist rates and attempting to reconstruct them after sample acquisition. Most such algorithms have parameters, for example the regularization parameter in LASSO, …
View article: Reconstruction of Sparse Signals under Gaussian Noise and Saturation
Reconstruction of Sparse Signals under Gaussian Noise and Saturation Open
Most compressed sensing algorithms do not account for the effect of saturation in noisy compressed measurements, though saturation is an important consequence of the limited dynamic range of existing sensors. The few algorithms that handle…
View article: A Compressed Sensing Approach to Pooled RT-PCR Testing for COVID-19 Detection
A Compressed Sensing Approach to Pooled RT-PCR Testing for COVID-19 Detection Open
We propose 'Tapestry', a single-round pooled testing method with application to COVID-19 testing using quantitative Reverse Transcription Polymerase Chain Reaction (RT-PCR) that can result in shorter testing time and conservation of reagen…
View article: A Compressed Sensing Approach to Group-testing for COVID-19 Detection
A Compressed Sensing Approach to Group-testing for COVID-19 Detection Open
We propose Tapestry, a novel approach to pooled testing with application to COVID-19 testing with quantitative Polymerase Chain Reaction (PCR) that can result in shorter testing time and conservation of reagents and testing kits. Tapestry …
View article: Low radiation tomographic reconstruction with and without template information
Low radiation tomographic reconstruction with and without template information Open
View article: Tapestry: A Single-Round Smart Pooling Technique for COVID-19 Testing
Tapestry: A Single-Round Smart Pooling Technique for COVID-19 Testing Open
The COVID-19 pandemic has strained testing capabilities worldwide. There is an urgent need to find economical and scalable ways to test more people. We present Tapestry , a novel quantitative nonadaptive pooling scheme to test many samples…
View article: Low radiation tomographic reconstruction with and without template\n information
Low radiation tomographic reconstruction with and without template\n information Open
Low-dose tomography is highly preferred in medical procedures for its reduced\nradiation risk when compared to standard-dose Computed Tomography (CT).\nHowever, the lower the intensity of X-rays, the higher the acquisition noise\nand hence…
View article: Restoration of Non-Rigidly Distorted Underwater Images Using a Combination of Compressive Sensing and Local Polynomial Image Representations
Restoration of Non-Rigidly Distorted Underwater Images Using a Combination of Compressive Sensing and Local Polynomial Image Representations Open
Images of static scenes submerged beneath a wavy water surface exhibit severe non-rigid distortions. The physics of water flow suggests that water surfaces possess spatio-temporal smoothness and temporal periodicity. Hence they possess a s…
View article: Tomographic reconstruction to detect evolving structures
Tomographic reconstruction to detect evolving structures Open
The need for tomographic reconstruction from sparse measurements arises when the measurement process is potentially harmful, needs to be rapid, or is uneconomical. In such cases, information from previous longitudinal scans of the same obj…
View article: Restoration of Non-rigidly Distorted Underwater Images using a\n Combination of Compressive Sensing and Local Polynomial Image Representations
Restoration of Non-rigidly Distorted Underwater Images using a\n Combination of Compressive Sensing and Local Polynomial Image Representations Open
Images of static scenes submerged beneath a wavy water surface exhibit severe\nnon-rigid distortions. The physics of water flow suggests that water surfaces\npossess spatio-temporal smoothness and temporal periodicity. Hence they possess\n…