Blind Sensor Calibration using Approximate Message Passing

The ubiquity of approximately sparse data has led a variety of com- munities to great interest in compressed sensing algorithms. Although these are very successful and well understood for linear measurements with additive noise, applying them on real data can be problematic if imperfect sensing devices introduce deviations from this ideal signal ac- quisition process, caused by sensor decalibration or failure. We propose a message passing algorithm called calibration approximate message passing (Cal-AMP) that can treat a variety of such sensor-induced imperfections. In addition to deriving the general form of the algorithm, we numerically investigate two particular settings. In the first, a fraction of the sensors is faulty, giving readings unrelated to the signal. In the second, sensors are decalibrated and each one introduces a different multiplicative gain to the measures. Cal-AMP shares the scalability of approximate message passing, allowing to treat big sized instances of these problems, and ex- perimentally exhibits a phase transition between domains of success and failure.Comment: 27 pages, 9 figure

S-AMP for Non-linear Observation Models

Recently we extended Approximate message passing (AMP) algorithm to be able to handle general invariant matrix ensembles. In this contribution we extend our S-AMP approach to non-linear observation models. We obtain generalized AMP (GAMP) algorithm as the special case when the measurement matrix has zero-mean iid Gaussian entries. Our derivation is based upon 1) deriving expectation propagation (EP) like algorithms from the stationary-points equations of the Gibbs free energy under first- and second-moment constraints and 2) applying additive free convolution in free probability theory to get low-complexity updates for the second moment quantities.Comment: 6 page

Bayes-Optimal Joint Channel-and-Data Estimation for Massive MIMO with Low-Precision ADCs

This paper considers a multiple-input multiple-output (MIMO) receiver with very low-precision analog-to-digital convertors (ADCs) with the goal of developing massive MIMO antenna systems that require minimal cost and power. Previous studies demonstrated that the training duration should be {\em relatively long} to obtain acceptable channel state information. To address this requirement, we adopt a joint channel-and-data (JCD) estimation method based on Bayes-optimal inference. This method yields minimal mean square errors with respect to the channels and payload data. We develop a Bayes-optimal JCD estimator using a recent technique based on approximate message passing. We then present an analytical framework to study the theoretical performance of the estimator in the large-system limit. Simulation results confirm our analytical results, which allow the efficient evaluation of the performance of quantized massive MIMO systems and provide insights into effective system design.Comment: accepted in IEEE Transactions on Signal Processin

Interkingdom Communication: The Role of Cyclic Di-nucleotides, Specifically 3’,5’-Cyclic Diguanylate, in Attraction and Immunomodulation of \u3ci\u3eCaenorhabditis elegans\u3c/i\u3e

Cyclic di-nucleotides are important secondary signaling molecules in bacteria that regulate a wide range of processes. Recently, the role of these molecules has expanded to the eukaryotic domain where they act in modulating the innate immune response. In this study, we have shown that Caenorhabditis elegans are able to detect and are attracted towards numerous signaling molecules produced by Vibrio cholerae, even though this bacterium kills the host at a high rate. Of these molecules, it seems that CDNs are playing an important role, specifically the 3’,5’-cyclic diguanylate (c-di-GMP), and the recently described hybrid molecule produced by V. cholerae, c-GMP-AMP (c-GAMP). The chemoattraction of C. elegans towards these molecules occur in a concentration dependent manner. However, c-di-GMP was the only CDN present in V. cholerae cell lysate or supernatant, revealing its importance in this novel communication pathway. C-di-GMP is sensed through C. elegans olfactory AWC neurons which then evokes a series of signal transduction pathways that lead to reduced activity of two key stress response transcription factors, SKN-1 and HSF-1, and a weakened innate immunity. Taken together, our study elucidates the role of c-di-GMP in interkingdom communication, i.e. bacteria produce c-di-GMP to attract a host and impair its immune response, which in turn promotes bacterial invasion and survival

Group Testing with Side Information via Generalized Approximate Message Passing

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 arranged into m < n pooled samples, where each pool is obtained by mixing a subset of the n individual samples. Infected individuals are then identified using a group testing algorithm. In this paper, we incorporate side information (SI) collected from contact tracing (CT) into nonadaptive/single-stage group testing algorithms. We generate different types of possible CT SI data by incorporating different possible characteristics of the spread of the disease. These data are fed into a group testing framework based on generalized approximate message passing (GAMP). Numerical results show that our GAMP-based algorithms provide improved accuracy. Compared to a loopy belief propagation algorithm, our proposed framework can increase the success probability by 0.25 for a group testing problem of n = 500 individuals with m = 100 pooled samples.Comment: arXiv admin note: substantial text overlap with arXiv:2106.02699, arXiv:2011.1418

An analysis of the Georgia agricultural marketing project, 1984

This paper provides an explorative descriptive analysis of the organization and operation of Georgia Agricultural Marketing Project (GAMP). Five factors relating to the success of cooperative enterprises were examined to determine whether GAMP is a viable cooperative enterprise. These are (1) organizational operation, (2) financing strategies, (3) farmer participation, (4) consumer participation, and (5) agribusiness challenge. The writer sets forth in the analysis and conclusion reasons why as presently organized, GAMP is not a viable cooperative enterprise. The writer also makes recommendations, where appropriate, regarding how GAMP might function more effectively. The main sources of information were reports prepared by the U.S. Department of Agriculture, Economics, Statistics, Cooperative Service Division. This division provides research, management, and educational assistance to cooperatives and other rural residents to strengthen their economic position. Furthermore, reports prepared by the U.S. General Accounting Office, the Annuals of Public and Cooperative Economy, the Yearbook of the American Institute of Cooperation and the Journal of Consumer Affairs, were used. Also, other periodicals, and books and cooperative enterprises were used

Optimal quantization for compressive sensing under message passing reconstruction

Abstract—We consider the optimal quantization of compressive sensing measurements along with estimation from quantized samples using generalized approximate message passing (GAMP). GAMP is an iterative reconstruction scheme inspired by the belief propagation algorithm on bipartite graphs which generalizes approximate message passing (AMP) for arbitrary measurement channels. Its asymptotic error performance can be accurately predicted and tracked through the state evolution formalism. We utilize these results to design mean-square optimal scalar quantizers for GAMP signal reconstruction and empirically demonstrate the superior error performance of the resulting quantizers. I