The Positional Burrows-Wheeler Transform (PBWT) is a data structure designed for efficiently representing and querying large collections of sequences, such as haplotype panels in genomics. Forward and backward stepping operations - analogues to LF- and FL-mapping in the traditional BWT - are fundamental to the PBWT, underpinning many algorithms based on the PBWT for haplotype matching and related analyses. Although the run-length encoded variant of the PBWT (also known as the μ-PBWT) achieves O(r̃)-word space usage, where r̃ is the total number of runs, no data structure supporting both forward and backward stepping in constant time within this space bound was previously known. In this paper, we consider the multi-allelic PBWT that is extended from its original binary form to a general ordered alphabet {0, … , σ-1}. We first establish bounds on the size r̃ and then introduce a new O(r̃)-word data structure built over a list of haplotypes {S_1, … , S_h}, each of length w, that supports constant-time forward and backward stepping. We further revisit two key applications - haplotype retrieval and prefix search - leveraging our efficient forward stepping technique. Specifically, we design an O(r̃)-word space data structure that supports haplotype retrieval in O(log log_w h + w) time. For prefix search, we present an O(h + r̃)-word data structure that answers queries in O(m' log log_w σ + occ) time, where m' denotes the length of the longest common prefix returned and occ denotes the number of haplotypes prefixed the longest prefix.

Recently, Cenzato et al. proposed a new text index, called the suffixient array, which is a subset of the suffix array and supports locating a single pattern occurrence or finding its maximal exact matches (MEMs), assuming random access to the input text T[1..n] is available. They show that, given the suffix array, the longest common prefix array, and the Burrows-Wheeler transform (BWT) of the reverse of T[1..n] over an alphabet {1,…,σ}, a suffixient array can be constructed in linear time. However, their construction algorithms require multiple scans of these arrays. When restricted to a single pass over the arrays, they present an alternative construction algorithm running in O(n + r log σ) time, where r is the number of runs in the BWT of the reversed text. In this paper, we present a new one-pass algorithm that constructs a suffixient array in linear time under the standard RAM model.

Two identical anonymous mobile agents have to meet at a node of the infinite oriented grid whose nodes are unlabeled. This problem is known as rendezvous. The agents execute the same deterministic algorithm. Time is divided into rounds, and in each round each agent can either stay idle at the current node or move to an adjacent node. An adversary places the agents at two nodes of the grid at a distance at most D , and wakes them up in possibly different rounds. Each agent starts executing the algorithm in its wakeup round. If agents cannot leave any marks on visited nodes then they can never meet, even if they start simultaneously at adjacent nodes and know it. Hence, we assume that each agent marks any unmarked node it visits, and that an agent can distinguish if a node it visits has been previously marked or not. (If agents are ants then marking a node means secreting a chemical known as pheromone that can be subsequently sniffed). The time of a rendezvous algorithm is the number of rounds between the wakeup of the later agent and rendezvous. We ask the question whether the capability of marking nodes enables the agents to meet, and if so, what is the fastest rendezvous algorithm. We consider this rendezvous problem under three scenarios. In the first scenario, agents know D but may start with arbitrary delay. In the second scenario, they start simultaneously but do not have any a priori knowledge. In the third, most difficult scenario, we do not make any of the above facilitating assumptions. Agents start with arbitrary delay and they do not have any a priori knowledge. We prove that in the first two scenarios rendezvous can be accomplished in time O ( D ) . This is clearly optimal. For the third scenario, we prove that there does not exist any rendezvous algorithm working in time o ( D 2 ) , and we show an algorithm working in time O ( D 2 ) . The above negative result shows a separation between the optimal complexity in the two easier scenarios and the optimal complexity in the most difficult scenario.

Computing the matching statistics of a string$P[1..m]$with respect to a text$T[1..n]$is a fundamental problem which has application to genome sequence comparison. In this paper, we study the problem of computing the matching statistics upon highly repetitive texts. We design three different data structures that are similar to LZ-compressed indexes. The space costs of all of them can be measured by$\gamma$, the size of the smallest string attractor [STOC'2018] and$\delta$, a better measure of repetitiveness [LATIN'2020].

In the two-dimensional orthogonal colored range counting problem, we preprocess a set, P, of n colored points on the plane, such that given an orthogonal query rectangle, the number of distinct colors of the points contained in this rectangle can be computed efficiently. For this problem, we design three new solutions, and the bounds of each can be expressed in some form of time-space tradeoff. By setting appropriate parameter values for these solutions, we can achieve new specific results with (the space costs are in words and ε is an arbitrary constant in (0,1)): - O(nlg³ n) space and O(√nlg^{5/2} n lg lg n) query time; - O(nlg² n) space and O(√nlg^{4+ε} n) query time; - O(n (lg² n)/(lg lg n)) space and O(√nlg^{5+ε} n) query time; - O(nlg n) space and O(n^{1/2+ε}) query time. A known conditional lower bound to this problem based on Boolean matrix multiplication gives some evidence on the difficulty of achieving near-linear space solutions with query time better than √n by more than a polylogarithmic factor using purely combinatorial approaches. Thus the time and space bounds in all these results are efficient. Previously, among solutions with similar query times, the most space-efficient solution uses O(nlg⁴ n) space to answer queries in O(√nlg⁸ n) time (SIAM. J. Comp. 2008). Thus the new results listed above all achieve improvements in space efficiency, while all but the last result achieve speed-up in query time as well.

Under the word RAM model, we design three data structures that can be constructed in $O(n\sqrt{\lg n})$ time over $n$ points in an $n \times n$ grid. The first data structure is an $O(n\lg^ε n)$-word structure supporting orthogonal range reporting in $O(\lg\lg n+k)$ time, where $k$ denotes output size and $ε$ is an arbitrarily small constant. The second is an $O(n\lg\lg n)$-word structure supporting orthogonal range successor in $O(\lg\lg n)$ time, while the third is an $O(n\lg^ε n)$-word structure supporting sorted range reporting in $O(\lg\lg n+k)$ time. The query times of these data structures are optimal when the space costs must be within $O(n\ polylog\ n)$ words. Their exact space bounds match those of the best known results achieving the same query times, and the $O(n\sqrt{\lg n})$ construction time beats the previous bounds on preprocessing. Previously, among 2d range search structures, only the orthogonal range counting structure of Chan and Pǎtraşcu (SODA 2010) and the linear space, $O(\lg^ε n)$ query time structure for orthogonal range successor by Belazzougui and Puglisi (SODA 2016) can be built in the same $O(n\sqrt{\lg n})$ time. Hence our work is the first that achieve the same preprocessing time for optimal orthogonal range reporting and range successor. We also apply our results to improve the construction time of text indexes.