Multi-Stage Robust Chinese Remainder Theorem

It is well-known that the traditional Chinese remainder theorem (CRT) is not robust in the sense that a small error in a remainder may cause a large error in the reconstruction solution. A robust CRT was recently proposed for a special case when the greatest common divisor (gcd) of all the moduli is more than 1 and the remaining integers factorized by the gcd of all the moduli are co-prime. In this special case, a closed-form reconstruction from erroneous remainders was proposed and a necessary and sufficient condition on the remainder errors was obtained. It basically says that the reconstruction error is upper bounded by the remainder error level $\tau$ if $\tau$ is smaller than a quarter of the gcd of all the moduli. In this paper, we consider the robust reconstruction problem for a general set of moduli. We first present a necessary and sufficient condition for the remainder errors for a robust reconstruction from erroneous remainders with a general set of muduli and also a corresponding robust reconstruction method. This can be thought of as a single stage robust CRT. We then propose a two-stage robust CRT by grouping the moduli into several groups as follows. First, the single stage robust CRT is applied to each group. Then, with these robust reconstructions from all the groups, the single stage robust CRT is applied again across the groups. This is then easily generalized to multi-stage robust CRT. Interestingly, with this two-stage robust CRT, the robust reconstruction holds even when the remainder error level $\tau$ is above the quarter of the gcd of all the moduli. In this paper, we also propose an algorithm on how to group a set of moduli for a better reconstruction robustness of the two-stage robust CRT in some special cases.Comment: 26 pages, 2 figure

An Information Theoretic Study for Noisy Compressed Sensing With Joint Sparsity Model-2

In this paper, we study a support set reconstruction problem in which the signals of interest are jointly sparse with a common support set, and sampled by joint sparsity model-2 (JSM-2) in the presence of noise. Using mathematical tools, we develop upper and lower bounds on the failure probability of support set reconstruction in terms of the sparsity, the ambient dimension, the minimum signal to noise ratio, the number of measurement vectors and the number of measurements. These bounds can be used to provide a guideline to determine the system parameters in various applications of compressed sensing with noisy JSM-2. Based on the bounds, we develop necessary and sufficient conditions for reliable support set reconstruction. We interpret these conditions to give theoretical explanations about the benefits enabled by joint sparsity structure in noisy JSM-2. We compare our sufficient condition with the existing result of noisy multiple measurement vectors model (MMV). As a result, we show that noisy JSM-2 may require less number of measurements than noisy MMV for reliable support set reconstruction

Coding With Action-dependent Side Information and Additional Reconstruction Requirements

Constrained lossy source coding and channel coding with side information problems which extend the classic Wyner-Ziv and Gel'fand-Pinsker problems are considered. Inspired by applications in sensor networking and control, we first consider lossy source coding with two-sided partial side information where the quality/availability of the side information can be influenced by a cost-constrained action sequence. A decoder reconstructs a source sequence subject to the distortion constraint, and at the same time, an encoder is additionally required to be able to estimate the decoder's reconstruction. Next, we consider the channel coding "dual" where the channel state is assumed to depend on the action sequence, and the decoder is required to decode both the transmitted message and channel input reliably. Implications on the fundamental limits of communication in discrete memoryless systems due to the additional reconstruction constraints are investigated. Single-letter expressions for the rate-distortion-cost function and channel capacity for the respective source and channel coding problems are derived. The dual relation between the two problems is discussed. Additionally, based on the two-stage coding structure and the additional reconstruction constraint of the channel coding problem, we discuss and give an interpretation of the two-stage coding condition which appears in the channel capacity expression. Besides the rate constraint on the message, this condition is a necessary and sufficient condition for reliable transmission of the channel input sequence over the channel in our "two-stage" communication problem. It is also shown in one example that there exists a case where the two-stage coding condition can be active in computing the capacity, and it thus can actively restrict the set of capacity achieving input distributions.Comment: submitted to IEEE Trans. Information Theor

Reconstruction of convex bodies from moments

We investigate how much information about a convex body can be retrieved from a finite number of its geometric moments. We give a sufficient condition for a convex body to be uniquely determined by a finite number of its geometric moments, and we show that among all convex bodies, those which are uniquely determined by a finite number of moments form a dense set. Further, we derive a stability result for convex bodies based on geometric moments. It turns out that the stability result is improved considerably by using another set of moments, namely Legendre moments. We present a reconstruction algorithm that approximates a convex body using a finite number of its Legendre moments. The consistency of the algorithm is established using the stability result for Legendre moments. When only noisy measurements of Legendre moments are available, the consistency of the algorithm is established under certain assumptions on the variance of the noise variables

Random and universal metric spaces

We introduce a model of the set of all Polish (=separable complete metric) spaces: the cone $\cal R$ of distance matrices, and consider geometric and probabilistic problems connected with this object. The notion of the universal distance matrix is defined and we proved that the set of such matrices is everywhere dense $G_{\delta}$ set in weak topology in the cone $\cal R$. Universality of distance matrix is the necessary and sufficient condition on the distance matrix of the countable everywhere dense set of so called universal Urysohn space which he had defined in 1924 in his last paper. This means that Urysohn space is generic in the set of all Polish spaces. Then we consider metric spaces with measures (metric triples) and define a complete invariant: its - matrix distribution. We give an intrinsic characterization of the set of matrix distributions, and using the ergodic theorem, give a new proof of Gromov's ``reconstruction theorem'. A natural construction of a wide class of measures on the cone $\cal R$ is given and for these we show that {\it with probability one a random Polish space is again the Urysohn space}. There is a close connection between these questions, metric classification of measurable functions of several arguments, and classification of the actions of the infinite symmetric group [V1,V2].Comment: 30 pages, substantially revised versio

Direct measurement of the biphoton Wigner function through two-photon interference

The Hong-Ou-Mandel (HOM) experiment was a benchmark in quantum optics, evidencing the quantum nature of the photon. In order to go deeper, and obtain the complete information about the quantum state of a system, for instance, composed by photons, the direct measurement or reconstruction of the Wigner function or other quasi--probability distribution in phase space is necessary. In the present paper, we show that a simple modification in the well-known HOM experiment provides the direct measurement of the Wigner function. We apply our results to a widely used quantum optics system, consisting of the biphoton generated in the parametric down conversion process. In this approach, a negative value of the Wigner function is a sufficient condition for non-gaussian entanglement between two photons. In the general case, the Wigner function provides all the required information to infer entanglement using well known necessary and sufficient criteria. We analyze our results using two examples of parametric down conversion processes taken from recent experiments. The present work offers a new vision of the HOM experiment that further develops its possibilities to realize fundamental tests of quantum mechanics involving decoherence and entanglement using simple optical set-ups.Comment: 5 pages, 2 figures plus supplementary information; submitted; comments welcom

Robust Compressed Sensing Under Matrix Uncertainties

Compressed sensing (CS) shows that a signal having a sparse or compressible representation can be recovered from a small set of linear measurements. In classical CS theory, the sampling matrix and representation matrix are assumed to be known exactly in advance. However, uncertainties exist due to sampling distortion, finite grids of the parameter space of dictionary, etc. In this paper, we take a generalized sparse signal model, which simultaneously considers the sampling and representation matrix uncertainties. Based on the new signal model, a new optimization model for robust sparse signal reconstruction is proposed. This optimization model can be deduced with stochastic robust approximation analysis. Both convex relaxation and greedy algorithms are used to solve the optimization problem. For the convex relaxation method, a sufficient condition for recovery by convex relaxation is given; For the greedy algorithm, it is realized by the introduction of a pre-processing of the sensing matrix and the measurements. In numerical experiments, both simulated data and real-life ECG data based results show that the proposed method has a better performance than the current methods.Comment: 17 pages, 8 figure

Randomized transmit and receive ultrasound tomography

A tomographic method is considered that forms images from sets of spatially randomized source signals and receiver sensitivities. The method is designed to allow image reconstruction for an extended number of transmitters and receivers in the presence noise and without plane wave approximation or otherwise approximation on the size or regularity of source and receiver functions. An overdetermined set of functions are formed from the Hadamard product between a Gaussian function and a uniformly distributed random number set. It is shown that this particular type of randomization tends to produce well-conditioned matrices whose pseudoinverses may be determined without implementing relaxation methods. When the inverted sets are applied to simulated first-order scattering from a Shepp-Logan phantom, successful image reconstructions are achieved for signal-to-noise ratios (SNR) as low as 1. Evaluation of the randomization approach is conducted by comparing condition numbers with other forms of signal randomization. Image quality resulting from tomographic reconstructions is then compared with an idealized synthetic aperture approach, which is subjected to a comparable SNR. By root-mean-square-difference comparisons it is concluded that - provided a sufficient level of oversampling - the dynamic transmit and dynamic receive approach produces superior images, particularly in the presence of low SNR.Comment: 16 pages, 7 figure

Spectral neighbor joining for reconstruction of latent tree models

A common assumption in multiple scientific applications is that the distribution of observed data can be modeled by a latent tree graphical model. An important example is phylogenetics, where the tree models the evolutionary lineages of a set of observed organisms. Given a set of independent realizations of the random variables at the leaves of the tree, a key challenge is to infer the underlying tree topology. In this work we develop Spectral Neighbor Joining (SNJ), a novel method to recover the structure of latent tree graphical models. Given a matrix that contains a measure of similarity between all pairs of observed variables, SNJ computes a spectral measure of cohesion between groups of observed variables. We prove that SNJ is consistent, and derive a sufficient condition for correct tree recovery from an estimated similarity matrix. Combining this condition with a concentration of measure result on the similarity matrix, we bound the number of samples required to recover the tree with high probability. We illustrate via extensive simulations that in comparison to several other reconstruction methods, SNJ requires fewer samples to accurately recover trees with a large number of leaves or long edges

Theoretical Bounds on Mate-Pair Information for Accurate Genome Assembly

Over the past two decades, a series of works have aimed at studying the problem of genome assembly: the process of reconstructing a genome from sequence reads. An early formulation of the genome assembly problem showed that genome reconstruction is NP-hard when framed as finding the shortest sequence that contains all observed reads. Although this original formulation is very simplistic and does not allow for mate-pair information, subsequent formulations have also proven to be NP-hard, and/or may not be guaranteed to return a correct assembly. In this paper, we provide an alternate perspective on the genome assembly problem by showing genome assembly is easy when provided with sufficient mate-pair information. Moreover, we quantify the number of mate-pair libraries necessary and sufficient for accurate genome assembly, in terms of the length of the longest repetitive region within a genome. In our analysis, we consider an idealized sequencing model where each mate-pair library generates pairs of error free reads with a fixed and known insert size at each position in the genome. Even in this idealized model, we show that accurate genome reconstruction cannot be guaranteed in the worst case unless at least roughly R/2L mate-pair libraries are produced, where R is the length of the longest repetitive region in the genome and L is the length of each read. On the other hand, if (R/L)+1 mate-pair libraries are provided, then a simple algorithm can be used to find a correct genome assembly easily in polynomial time. Although (R/L)+1 mate-pair libraries can be too much to produce in practice, the previous bounds only hold in the worst case. In our last result, we show that if additional conditions hold on a genome, a correct assembly can be guaranteed with only O(log (R/L)) mate-pair libraries