Vector Recovery Research Articles

Deep reinforcement learning-based attitude control for spacecraft using control moment gyros

This paper addresses the development of an attitude control system that steers control moment gyros (CMGs) based on deep reinforcement learning (DRL) for agile spacecraft. The proposed DRL-based attitude control system learns CMG steering strategies to achieve the desired attitude, thus potentially bypassing the singularity issues inherent in the CMG cluster. In particular, it is designed in two-phases to apply the DRL technique efficiently. In the first phase, the attitude control is performed based on DRL up to a certain tolerance, after which it switches to conventional control and steering law for stabilization in the second phase. The rapid pointing capability of the proposed DRL-based attitude control system is demonstrated for an agile spacecraft equipped with pyramid-type single gimbal control moment gyros. Additionally, in realistic scenarios of pointing multiple targets on the ground, the momentum vector recovery that the CMG system needs to consider is also briefly discussed.

Role of Microfiltration Membrane Morphology on Nanoparticle Purification to Enhance Downstream Purification of Viral Vectors.

In the rapidly advancing realms of gene therapy and biotechnology, the efficient purification of viral vectors is pivotal for ensuring the safety and efficacy of gene therapies. This study focuses on optimizing membrane selection for viral vector purification by evaluating key properties, including porosity, thickness, pore structure, and hydrophilicity. Notably, we employed adeno-associated virus (AAV)-sized nanoparticles (20 nm), 200 nm particles, and bovine serum albumin (BSA) to model viral vector harvesting. Experimental data from constant pressure normal flow filtration (NFF) at 1 and 2 bar using four commercial flat sheet membranes revealed distinct fouling behaviors. Symmetric membranes predominantly showed internal and external pore blockage, while asymmetric membranes formed a cake layer on the surface. Hydrophilicity exhibited a positive correlation with recovery, demonstrating an enhanced recovery with increased hydrophilicity. Membranes with higher porosity and interpore connectivity showcased superior throughput, reduced operating time, and increased recovery. Asymmetric polyether sulfone (PES) membranes emerged as the optimal choice, achieving ∼100% recovery of AAV-sized particles, an ∼44% reduction in model cell debris (200 nm particles), an ∼35% decrease in BSA, and the fastest operating time of all membranes tested. This systematic investigation into fouling behaviors and membrane properties not only informs optimal conditions for viral vector recovery but also lays the groundwork for advancing membrane-based strategies in bioprocessing.

Production and Purification of Adeno-Associated Viral Vectors (AAVs) Using Orbitally Shaken HEK293 Cells.

Here, we describe methods for the production of adeno-associated viral (AAV) vectors by transient transfection of HEK293 cells grown in serum-free medium using orbital shaken bioreactors and the subsequent purification of vector particles. The protocol for expression of AAV components is based on polyethyleneimine (PEI)-mediated transfection of a three-plasmid system and is specified for production in milliliter-to-liter scales. After PEI and plasmid DNA (pDNA) complex formation, the diluted cell culture is transfected without a prior concentration step or medium exchange. Following a 7-day batch process, cell cultures are further processed using a set of methods for cell lysis and vector recovery. Methods for the purification of viral particles are described, including immunoaffinity and anion-exchange chromatography, ultrafiltration, as well as digital PCR to quantify the concentration of vector particles.

Bayesian Algorithms for Kronecker-structured Sparse Vector Recovery With Application to IRS-MIMO Channel Estimation

Bayesian Algorithms for Kronecker-structured Sparse Vector Recovery With Application to IRS-MIMO Channel Estimation

Block sparse vector recovery for compressive sensing via ℓ1−αℓq$\ell _1-\alpha \ell _q$‐minimization Model

AbstractThis paper solves the problem of block sparse vector recovery using the block ‐minimization model. Based on the block restricted isometry property (B‐RIP) condition, exact block sparse vector recovery result is obtained. The theoretical bound for the block ‐minimization model are also obtained when measurements are depraved by the noises.

Codes for exact support recovery of sparse vectors from inaccurate linear measurements and their decoding

We construct codes that allow to exactly recover the support of an unknown sparse vector with almost equal absolute values of all nonzero coordinates given results of linear measurements in the presence of noise with ℓp-norm bounded from above. We propose a decoding algorithm with asymptotically minimum complexity.

Depth filtration for clarification of intensified lentiviral vector suspension cell culture.

Depth filtration significantly impacts efficiency of lentiviral (LV) vector purification process. However, it is often deprioritized in the overall scope of viral vector manufacturing process optimization. The demand for LV vectors has increased with the rise in disease indications, making it crucial to improve current manufacturing processes. Upstream bioreactor process intensification has enabled cell densities of over 107 viable cells/mL, creating challenges for harvest unit operations. The larger size of LV vectors and their physiochemical similarity to host cell-DNA (HC-DNA) and poor clarification performance causes significant challenges for the subsequent chromatography-based purifications. As a result, a robust and scalable harvest of LV process is needed, especially for LV in vivo therapeutic quality needs. In this study, we systematically evaluated the overlooked yet important issue of depth filtration systems to improve enveloped LV functional vector recovery. We found that an established depth filtration system in process A that provided 94% (n = 6) LV functional recovery could not be translated to intensified Process B cell culture. Hence, the depth filtration process became a bottleneck for the purification performance in an intensified process. We demonstrated an improvement in LV functional vector recovery from 34% to 82% via filter train optimization for an intensified suspension cell culture system (>107 cells/mL with higher titer), while still maintaining a loading throughput of ≥82 L/m2 and turbidity ≤20 NTU. It was demonstrated that the two or three-stage depth filtration scheme is scalable and more suitable for high cell density culture for large scale for LV manufacturing process.

Optimization of rAAV capture step purification using SO3 monolith chromatography.

Adeno-associated virus (AAV) vectors are crucial tools for gene therapy applications. As AAVs are administered in vivo, stringent purity requirements must be met, necessitating the development of various downstream processing strategies in accordance with regulatory guidelines. In this context, we focus on the non-affinity serotype-independent recombinant AAV (rAAV) capture step, which involves the use of Convective Interaction Media (CIM) cation-exchange SO3 monoliths. We analyzed differentially pretreated viral samples obtained from the Sf9 cell line and applied these samples to the capture SO3 chromatography step. We conducted screening experiments using CIM SO3 0.05mL monolithic 96-well plates with buffers of varying pH, sodium chloride concentrations, and the inclusion of poloxamer 188, aiming to select the optimal binding mobile phase. Dynamic binding capacity was defined for different pretreatments and the optimal conditions were subsequently retested using the industrial purification CIMmultus line. The results demonstrated a high overall vector recovery (51%) and a significant reduction in impurities (99.98% for protein reduction and 99.25% for DNA reduction) using the selected capture step parameters, thereby confirming the successful optimization of the rAAV capture step in the downstream process using monoliths.

Insights into product and process related challenges of lentiviral vector bioprocessing.

Lentiviral vectors (LVs) are used in advanced therapies to transduce recipient cells for long term gene expression for therapeutic benefit. The vector is commonly pseudotyped with alternative viral envelope proteins to improve tropism and is selected for enhanced functional titers. However, their impact on manufacturing and the success of individual bioprocessing unit operations is seldom demonstrated. To the best of our knowledge, this is the first study on the processability of different Lentiviral vector pseudotypes. In this work, we compared three envelope proteins commonly pseudotyped with LVs across manufacturing conditions such as temperature and pump flow and across steps common to downstream processing. We have shown impact of filter membrane chemistry on vector recoveries with differing envelopes during clarification and observed complete vector robustness in high shear manufacturing environments using ultra scale-down technologies. The impact of shear during membrane filtration in a tangential flow filtration-mimic showed the benefit of employing higher shear rates, than currently used in LV production, to increase vector recovery. Likewise, optimized anion exchange chromatography purification in monolith format was determined. The results contradict a common perception that lentiviral vectors are susceptible to shear or high salt concentration (up to 1.7 M). This highlights the prospects of improving LV recovery by evaluating manufacturing conditions that contribute to vector losses for specific production systems.

Multitarget Enumeration and Localization in Distributed MIMO Radar Based on Energy Modeling and Compressive Sensing

Target localization is one of the most important research topics in the field of radar signal processing. In this paper, the problem of multitarget enumeration and localization in the distributed multiple-input multiple-output (MIMO) radar with noncoherent processing mode is investigated. We first analyze the theoretical bound of the multitarget localization accuracy under the discrete time signal model and the Swerling 1 target model. It is determined by the Cram'r-Rao lower bound (CRLB) at low signal-to-noise ratio (SNR) and the sampling lower bound (SLB) when the SNR is high. Furthermore, an innovative multitarget enumeration and localization scheme is developed, which is based on the energy modeling of the multiple transmitter-receiver paths and the compressive sensing theory. To solve the sparse vector recovery issue, we design a lightweight iterative greedy pursuit algorithm including the similarity evaluation strategy. In addition, an iterative-based target position refinement process is designed to alleviate the off-grid problem caused by the spatial discretization. The proposal utilizes the samples of the raw signals and belongs to the category of the direct localization. Nevertheless, it has significantly higher computational efficiency and lower data communication burden than the conventional direct localization methods, while avoiding the complex data association encountered by the indirect localization methods. Finally, the simulation results validate the effectiveness and robustness of the proposed method.

Support recovery with Projected Stochastic Gates: Theory and application for linear models

Consider the problem of simultaneous estimation and support recovery of the coefficient vector in a linear data model with additive Gaussian noise. We study the problem of estimating the model coefficients based on a recently proposed non-convex regularizer, namely the stochastic gates (STG) (Yamada et al., 2020). We suggest a new projection-based algorithm for solving the STG regularized minimization problem, and prove convergence and support recovery guarantees of the STG-estimator for a range of random and non-random design matrix setups. Our new algorithm has been shown to outperform the existing STG algorithm and other classical estimators for support recovery in various real and synthetic data analyses.

Random sections of [formula omitted]-ellipsoids, optimal recovery and Gelfand numbers of diagonal operators

We study the circumradius of a random section of an ℓp-ellipsoid, 0<p≤∞, and compare it with the minimal circumradius over all sections with subspaces of the same codimension. Our main result is an upper bound for random sections, which we prove using techniques from asymptotic geometric analysis if 1≤p≤∞ and compressed sensing if 0<p≤1. This can be interpreted as a bound on the quality of random (Gaussian) information for the recovery of vectors from an ℓp-ellipsoid for which the radius of optimal information is given by the Gelfand numbers of a diagonal operator. In the case where the semiaxes decay polynomially and 1≤p≤∞, we conjecture that, as the amount of information increases, the radius of random information either decays like the radius of optimal information or is bounded from below by a constant, depending on whether the exponent of decay is larger than the critical value 1−1p or not. If 1≤p≤2, we prove this conjecture by providing a matching lower bound. This extends the recent work of Hinrichs et al. [Random sections of ellipsoids and the power of random information, Trans. Amer. Math. Soc., 2021] for the case p=2.

Modeling and channel estimation for piezo-acoustic backscatter assisted underwater acoustic communications

Relying on direct and converse piezoelectric effects, piezo-acoustic backscatter (PAB) technology reflects ambient acoustic signals to enable underwater backscatter communications at near-zero power, which was first realized through a prototype. In this paper, we propose a mathematical model of the PAB assisted underwater acoustic (UWA) communication, and address the sparse channel estimation problem. First, we present a five-stage backscatter process to derive the backscatter coefficient, and propose the channel model for the shallow-water communications. Then, we formulate the shallow-water acoustic channel estimation problem as a sparse vector recovery one according to the compressed sensing theory, and leverage the orthogonal matching pursuit (OMP) algorithm to obtain the channel estimator. Finally, simulation results are provided to corroborate our proposed studies.

Sparse Group Lasso: Optimal Sample Complexity, Convergence Rate, and Statistical Inference.

We study sparse group Lasso for high-dimensional double sparse linear regression, where the parameter of interest is simultaneously element-wise and group-wise sparse. This problem is an important instance of the simultaneously structured model - an actively studied topic in statistics and machine learning. In the noiseless case, matching upper and lower bounds on sample complexity are established for the exact recovery of sparse vectors and for stable estimation of approximately sparse vectors, respectively. In the noisy case, upper and matching minimax lower bounds for estimation error are obtained. We also consider the debiased sparse group Lasso and investigate its asymptotic property for the purpose of statistical inference. Finally, numerical studies are provided to support the theoretical results.

Optimized formulation buffer preserves adeno-associated virus-9 infectivity after 4 °C storage and freeze/thawing cycling

Adeno-associated virus (AAV) have long been one of the most common and versatile vectors for in vitro and in vivo gene transfer. AAV production protocols are complex and time consuming, one key concern is the recovery and infectivity of viral vector after purification. The buffer used in the storage of AAV at 4 °C and − 80 °C is a crucial factor and methods to improve it have been thoroughly investigated. Viral core facilities have developed formulas using either 0.001% Pluronic F68 or 5% sorbitol in their storage buffers based on the results of this research. Interestingly, few use formulations that include both a non-ionic surfactant and cryopreservative. In this study, AAV9 stored at 4 °C and at − 80 °C in the standard buffers is compared to a buffer that contains 5% glycerol and 0.001% Pluronic F68. By viral genome quantitation with qPCR, all three formulations show the same extent of viral titer loss at 4 °C, while after several cycles of freeze/thaws at − 80 °C, the viral recovery and infectivity in the preparation with both glycerol and Pluronic F68 was most stable compared to the other buffers.

Massive Grant-Free OFDMA With Timing and Frequency Offsets

In the massive grant-free orthogonal frequency division multiple access (OFDMA), the timing and frequency offsets between users impose new challenges on joint active user detection (AUD) and channel estimation (CE) for the subsequent data recovery. In the asynchronous OFDMA, the timing and frequency offset effects can be modeled as the phase-shifting on the pilot matrix. As such, by constructing the measurement matrix with timing and frequency offsets, the joint estimation problem can be formulated as a multiple measurement vector (MMV) recovery problem with structured sparsity. However, such structured sparsity cannot be tackled by the existing compressed sensing (CS) techniques. To address this issue, we develop an efficient structured generalized approximate message passing (S-GAMP) algorithm, which includes the parallel AMP-MMV algorithm as a particular case. To deal with the high dimensionality of the measurement matrix, we propose the dynamic S-GAMP algorithm with a dynamic measurement matrix to reduce the computational complexity. Simulation results confirm the superiority of the proposed algorithms in grant-free OFDMA with both timing and frequency offsets.

Recovery from Power Sums

We study the problem of recovering a collection of n numbers from the evaluation of m power sums. This yields a system of polynomial equations, which can be underconstrained (m < n), square (m = n), or overconstrained (m > n). Fibers and images of power sum maps are explored in all three regimes, and in settings that range from complex and projective to real and positive. This involves surprising deviations from the Bézout bound, and the recovery of vectors from length measurements by p-norms.

Dictionary-sparse recovery from heavy-tailed measurements

Abstract The recovery of signals that are sparse not in a given basis, but rather sparse with respect to an over-complete dictionary is one of the most flexible settings in the field of compressed sensing with numerous applications. As in the standard compressed sensing setting, it is possible that the signal can be reconstructed efficiently from a few linear measurements, for example, by the so-called $\ell _1$-synthesis method. However, it has been less well understood which measurement matrices provably work for this setting. Whereas in the standard setting, it has been shown that even certain heavy-tailed measurement matrices can be used in the same sample complexity regime as Gaussian matrices, comparable results are only available for the restrictive class of sub-Gaussian measurement matrices as far as the recovery of dictionary-sparse signals via $\ell _1$-synthesis is concerned. In this work, we fill this gap and establish optimal guarantees for the recovery of vectors that are (approximately) sparse with respect to a dictionary via the $\ell _1$-synthesis method from linear, potentially noisy measurements for a large class of random measurement matrices. In particular, we show that random measurements that fulfill only a small-ball assumption and a weak moment assumption, such as random vectors with i.i.d. Student-$t$ entries with a logarithmic number of degrees of freedom, lead to comparable guarantees as (sub-)Gaussian measurements. Our results apply for a large class of both random and deterministic dictionaries. As a technical tool, we show a bound on the expectation of the sum of squared order statistics under very general assumptions, which might be of independent interest. As a corollary of our results, we also obtain a slight improvement on the weakest assumption on a measurement matrix with i.i.d. rows sufficient for uniform recovery in standard compressed sensing, improving on results by Lecué and Mendelson and by Dirksen, Lecué and Rauhut.

Comments on “Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO”

In the generalized multiple measurement vectors (GMMV) setup of compressive sensing, we consider the problem of jointly recovering multiple sparse signal vectors sharing a common sparsity pattern based on joint analysis of their linear measurements. The important aspect of the GMMV setup is that the linear measurements of different sparse signal vectors are acquired by using different sensing matrices. It is not yet well understood how the GMMV setup affects identifiability of sparse signal vectors from the measurements, in particular compared to the single measurement vector (SMV) setup wherein we perform recovery of each sparse signal vector independently. Gao et al. [1] presented the first theorem on identifiability of sparse signal vectors in the GMMV setup, in which it is stated that the GMMV setup allows signal vectors with more nonzero rows to be identifiable compared to the SMV setup. In this correspondence, we aim to present a contrasting perspective on sparse signal identifiability in the GMMV setup. In particular, it is shown that the GMMV setup does not result in an improved identifiability condition for some signal vectors even when their entries are diverse.

A necessary and sufficient condition for sparse vector recovery via ℓ1 − ℓ2 minimization

In this paper, we focus on ℓ 1 − ℓ 2 minimization model, i.e., investigating the nonconvex model: min ⁡ ‖ x ‖ 1 − ‖ x ‖ 2 s.t. A x = y and provide a null space property of the measurement matrix A such that a vector x can be recovered from Ax via ℓ 1 − ℓ 2 minimization. The ℓ 1 − ℓ 2 minimization model was first proposed by E.Esser, et al (2013) [8] . As a nonconvex model, it is well known that global minimizer and local minimizer are usually inconsistent. In this paper, we present a necessary and sufficient condition for the measurement matrix A such that (1) a vector x can be recovered from Ax via ℓ 1 − ℓ 2 local minimization ( Theorem 4 ); (2) any k -sparse vector x can be recovered from Ax via ℓ 1 − ℓ 2 local minimization ( Theorem 5 ); (3) any k -sparse vector x can be recovered from Ax via ℓ 1 − ℓ 2 global minimization ( Theorem 6 ).