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In this paper, we explore constructions for codes that correct two bursts of deletions, with each burst having length exactly $b$. Previously, the best known construction, derived using the syndrome compression technique, achieved a redundancy of at most $7\log n+O\left(\log n/\log\log n\right)$ bits. In this work, we present new constructions for all $q\ge 2$ that achieve redundancy at most $7\log n+O(\log\log n)$ when $b>1$. Additionally, for $b=1$, we provide a new construction of $q$-ary two-deletion correcting codes with redundancy $5\log n+O(\log\log n)$ for all $q>2$., Comment: Typos/minor errors were corrected. Some explanations were added

In response to the evolving landscape of data storage, researchers have increasingly explored non-traditional platforms, with DNA-based storage emerging as a cutting-edge solution. Our work is motivated by the potential of in-vivo DNA storage, known for its capacity to store vast amounts of information efficiently and confidentially within an organism's native DNA. While promising, in-vivo DNA storage faces challenges, including susceptibility to errors introduced by mutations. To understand the long-term behavior of such mutation systems, we investigate the frequency of $k$-tuples after multiple mutation applications. Drawing inspiration from related works, we generalize results from the study of mutation systems, particularly focusing on the frequency of $k$-tuples. In this work, we provide a broad analysis through the construction of a specialized matrix and the identification of its eigenvectors. In the context of substitution and duplication systems, we leverage previous results on almost sure convergence, equating the expected frequency to the limiting frequency. Moreover, we demonstrate convergence in probability under certain assumptions.

Private information retrieval (PIR) codes and batch codes are two important types of codes that are designed for coded distributed storage systems and private information retrieval protocols. These codes have been the focus of much attention in recent years, as they enable efficient and secure storage and retrieval of data in distributed systems. In this paper, we introduce a new class of codes called \emph{$(s,t)$-batch codes}. These codes are a type of storage codes that can handle any multi-set of $t$ requests, comprised of $s$ distinct information symbols. Importantly, PIR codes and batch codes are special cases of $(s,t)$-batch codes. The main goal of this paper is to explore the relationship between the number of redundancy symbols and the $(s,t)$-batch code property. Specifically, we establish a lower bound on the number of redundancy symbols required and present several constructions of $(s,t)$-batch codes. Furthermore, we extend this property to the case where each request is a linear combination of information symbols, which we refer to as \emph{functional $(s,t)$-batch codes}. Specifically, we demonstrate that simplex codes are asymptotically optimal functional $(s,t)$-batch codes, in terms of the number of redundancy symbols required, under certain parameter regime., Comment: 25 pages

A storage code is an assignment of symbols to the vertices of a connected graph $G(V,E)$ with the property that the value of each vertex is a function of the values of its neighbors, or more generally, of a certain neighborhood of the vertex in $G$. In this work we introduce a new construction method of storage codes, enabling one to construct new codes from known ones via an interleaving procedure driven by resolvable designs. We also study storage codes on $\mathbb Z$ and ${\mathbb Z}^2$ (lines and grids), finding closed-form expressions for the capacity of several one and two-dimensional systems depending on their recovery set, using connections between storage codes, graphs, anticodes, and difference-avoiding sets.

In this paper, we present a novel communication channel, called the absorption channel, inspired by information transmission in neurons. Our motivation comes from in-vivo nano-machines, emerging medical applications, and brain-machine interfaces that communicate over the nervous system. Another motivation comes from viewing our model as a specific deletion channel, which may provide a new perspective and ideas to study the general deletion channel. For any given finite alphabet, we give codes that can correct absorption errors. For the binary alphabet, the problem is relatively trivial and we can apply binary (multiple-) deletion correcting codes. For single-absorption error, we prove that the Varshamov-Tenengolts codes can provide a near-optimal code in our setting. When the alphabet size $q$ is at least $3$, we first construct a single-absorption correcting code whose redundancy is at most $3\log_q(n)+O(1)$. Then, based on this code and ideas introduced in \cite{Gabrys2022IT}, we give a second construction of single-absorption correcting codes with redundancy $\log_q(n)+12\log_q\log_q(n)+O(1)$, which is optimal up to an $O\left(\log_q\log_q(n)\right)$. Finally, we apply the syndrome compression technique with pre-coding to obtain a subcode of the single-absorption correcting code. This subcode can combat multiple-absorption errors and has low redundancy. For each setup, efficient encoders and decoders are provided.

We first give a construction of binary $t_1$-deletion-$t_2$-insertion-burst correcting codes with redundancy at most $\log(n)+(t_1-t_2-1)\log\log(n)+O(1)$, where $t_1\ge 2t_2$. Then we give an improved construction of binary codes capable of correcting a burst of $4$ non-consecutive deletions, whose redundancy is reduced from $7\log(n)+2\log\log(n)+O(1)$ to $4\log(n)+6\log\log(n)+O(1)$. Lastly, by connecting non-binary $b$-burst-deletion correcting codes with binary $2b$-deletion-$b$-insertion-burst correcting codes, we give a new construction of non-binary $b$-burst-deletion correcting codes with redundancy at most $\log(n)+(b-1)\log\log(n)+O(1)$. This construction is different from previous results., Comment: Results are covered by others' work

Motivated by applications in polymer-based data storage, we study the problem of reconstructing a string from part of its composition multiset. We give a full description of the structure of the strings that cannot be uniquely reconstructed (up to reversal) from their multiset of all of their prefix-suffix compositions. Leveraging this description, we prove that for all $n\ge 6$, there exists a string of length $n$ that cannot be uniquely reconstructed up to reversal. Moreover, for all $n\ge 6$, we explicitly construct the set consisting of all length $n$ strings that can be uniquely reconstructed up to reversal. As a by product, we obtain that any binary string can be constructed using Dyck strings and Catalan-Bertrand strings. For any given string $\bm{s}$, we provide a method to explicitly construct the set of all strings with the same prefix-suffix composition multiset as $\bm{s}$, as well as a formula for the size of this set. As an application, we construct a composition code of maximal size. Furthermore, we construct two classes of composition codes which can respectively correct composition missing errors and mass reducing substitution errors. In addition, we raise two new problems: reconstructing a string from its composition multiset when at most a constant number of substring compositions are lost; reconstructing a string when only given its compositions of substrings of length at most $r$. For each of these setups, we give suitable codes under some conditions., Comment: 25 pages; errors corrected

In the recent years, DNA has emerged as a potentially viable storage technology. DNA synthesis, which refers to the task of writing the data into DNA, is perhaps the most costly part of existing storage systems. Accordingly, this high cost and low throughput limits the practical use in available DNA synthesis technologies. It has been found that the homopolymer run (i.e., the repetition of the same nucleotide) is a major factor affecting the synthesis and sequencing errors. Quite recently, [26] studied the role of batch optimization in reducing the cost of large scale DNA synthesis, for a given pool $\mathcal{S}$ of random quaternary strings of fixed length. Among other things, it was shown that the asymptotic cost savings of batch optimization are significantly greater when the strings in $\mathcal{S}$ contain repeats of the same character (homopolymer run of length one), as compared to the case where strings are unconstrained. Following the lead of [26], in this paper, we take a step forward towards the theoretical understanding of DNA synthesis, and study the homopolymer run of length $k\geq1$. Specifically, we are given a set of DNA strands $\mathcal{S}$, randomly drawn from a natural Markovian distribution modeling a general homopolymer run length constraint, that we wish to synthesize. For this problem, we prove that for any $k\geq 1$, the optimal reference strand, minimizing the cost of DNA synthesis is, perhaps surprisingly, the periodic sequence $\overline{\mathsf{ACGT}}$. It turns out that tackling the homopolymer constraint of length $k\geq2$ is a challenging problem; our main technical contribution is the representation of the DNA synthesis process as a certain constrained system, for which string techniques can be applied., Comment: 21 pages

Motivated by the established notion of storage codes, we consider sets of infinite sequences over a finite alphabet such that every $k$-tuple of consecutive entries is uniquely recoverable from its $l$-neighborhood in the sequence. We address the problem of finding the maximum growth rate of the set, which we term capacity, as well as constructions of explicit families that approach the optimal rate. The techniques that we employ rely on the connection of this problem with constrained systems. In the second part of the paper we consider a modification of the problem wherein the entries in the sequence are viewed as random variables over a finite alphabet that follow some joint distribution, and the recovery condition requires that the Shannon entropy of the $k$-tuple conditioned on its $l$-neighborhood be bounded above by some $\epsilon>0.$ We study properties of measures on infinite sequences that maximize the metric entropy under the recoverability condition. Drawing on tools from ergodic theory, we prove some properties of entropy-maximizing measures. We also suggest a procedure of constructing an $\epsilon$-recoverable measure from a corresponding deterministic system., Comment: Minor changes from v1. This is a final version (to appear in IEEE Transactions on Information Theory)

In this paper we consider the problem of encoding data into \textit{repeat-free} sequences in which sequences are imposed to contain any $k$-tuple at most once (for predefined $k$). First, the capacity of the repeat-free constraint are calculated. Then, an efficient algorithm, which uses two bits of redundancy, is presented to encode length-$n$ sequences for $k=2+2\log (n)$. This algorithm is then improved to support any value of $k$ of the form $k=a\log (n)$, for $1

We introduce a dynamical model of node repair in distributed storage systems wherein the storage nodes are subjected to failures according to independent Poisson processes. The main parameter that we study is the time-average capacity of the network in the scenario where a fixed subset of the nodes support a higher repair bandwidth than the other nodes. The sequence of node failures generates random permutations of the nodes in the encoded block, and we model the state of the network as a Markov random walk on permutations of $n$ elements. As our main result we show that the capacity of the network can be increased compared to the static (worst-case) model of the storage system, while maintaining the same (average) repair bandwidth, and we derive estimates of the increase. We also quantify the capacity increase in the case that the repair center has information about the sequence of the recently failed storage nodes., Comment: 27 pages; final version

We study random string-duplication systems, which we call P\'olya string models. These are motivated by DNA storage in living organisms, and certain random mutation processes that affect their genome. Unlike previous works that study the combinatorial capacity of string-duplication systems, or various string statistics, this work provides exact capacity or bounds on it, for several probabilistic models. In particular, we study the capacity of noisy string-duplication systems, including the tandem-duplication, end-duplication, and interspersed-duplication systems. Interesting connections are drawn between some systems and the signature of random permutations, as well as to the beta distribution common in population genetics.

We find a new formula for the limit of the capacity of certain sequences of multidimensional semiconstrained systems as the dimension tends to infinity. We do so by generalizing the notion of independence entropy, originally studied in the context of constrained systems, to the study of semiconstrained systems. Using the independence entropy, we obtain new lower bounds on the capacity of multidimensional semiconstrained systems in general, and $d$-dimensional axial-product systems in particular. In the case of the latter, we prove our bound is asymptotically tight, giving the exact limiting capacity in terms of the independence entropy. We show the new bound improves upon the best-known bound in a case study of $(0,k,p)$-RLL., Comment: 31 pages

Semiconstrained systems were recently suggested as a generalization of constrained systems, commonly used in communication and data-storage applications that require certain offending subsequences be avoided. In an attempt to apply techniques from constrained systems, we study sequences of constrained systems that are contained in, or contain, a given semiconstrained system, while approaching its capacity. In the case of contained systems we describe to such sequences resulting in constant-to-constant bit-rate block encoders and sliding-block encoders. Surprisingly, in the case of containing systems we show that a "generic" semiconstrained system is never contained in a proper fully-constrained system., Comment: 15 pages

When transmitting information over a noisy channel, two approaches, dating back to Shannon's work, are common: assuming the channel errors are independent of the transmitted content and devising an error-correcting code, or assuming the errors are data dependent and devising a constrained-coding scheme that eliminates all offending data patterns. In this paper we analyze a middle road, which we call a semiconstrained system. In such a system, which is an extension of the channel with cost constraints model, we do not eliminate the error-causing sequences entirely, but rather restrict the frequency in which they appear. We address several key issues in this study. The first is proving closed-form bounds on the capacity which allow us to bound the asymptotics of the capacity. In particular, we bound the rate at which the capacity of the semiconstrained $(0,k)$-RLL tends to $1$ as $k$ grows. The second key issue is devising efficient encoding and decoding procedures that asymptotically achieve capacity with vanishing error. Finally, we consider delicate issues involving the continuity of the capacity and a relaxation of the definition of semiconstrained systems.

The Ising channel, which was introduced in 1990, is a channel with memory that models Inter-Symbol interference. In this paper we consider the Ising channel with feedback and find the capacity of the channel together with a capacity-achieving coding scheme. To calculate the channel capacity, an equivalent dynamic programming (DP) problem is formulated and solved. Using the DP solution, we establish that the feedback capacity is the expression $C=(\frac{2H_b(a)}{3+a})\approx 0.575522$ where $a$ is a particular root of a fourth-degree polynomial and $H_b(x)$ denotes the binary entropy function. Simultaneously, $a=\arg \max_{0\leq x \leq 1} (\frac{2H_b(x)}{3+x})$. Finally, a simple, error-free, capacity-achieving coding scheme is provided together with outlining a strong connection between the DP results and the coding scheme.