A suffixient array is a novel data structure that, when combined with an index providing direct access on a text T, allows us to answer a variety of pattern matching queries. In this work, we show how to compute a smallest suffixient array for T[1… n] in O(nlog σ/√(log n)+min(r,r̅)log^εn) time for any ε> 0, where σ is the alphabet size of T and r and r̅ are the numbers of equal-letter runs of the Burrows-Wheeler transforms of T and its reverse T, respectively. This time complexity becomes sublinear when σ is small enough and min(r,r̅)=o(n/log^εn), yielding an asymptotic improvement over state-of-the-art algorithms. We also present a series of connected algorithmic results.

Suffixient arrays are recent structures that have attracted attention because they offer relevant pattern matching functionality in less asymptotic space than the Run-Length BWT, the de-facto standard to index highly repetitive string collections. Various algorithms exist for building them from the suffix array data structures. We present the first construction algorithm that is (i) linear-time, (ii) one-pass over the structures, and (iii) implemented and practical. This makes the construction particularly useful on large text collections, which we demonstrate empirically by showing that it dominates the space/time tradeoff map of the implemented constructions.

Contextual pattern matching is the task of, given a pattern P[1,m], a context length λ, and a text T[1,n], find all the occ distinct contexts in which P occurs in T, the context being the λ symbols preceding and the λ symbols following the occurrence; a text position where each context occurs must be output. While the problem can be solved in optimal time O(m+occ) using O(n)-space precomputed data structures on T, this type of search is particularly relevant on large repetitive text collections, where O(n) space can be prohibitive. We present the first optimal-time solution that runs in compressed space, namely that of a symmetric CDAWG (SCDAWG) of T. Further, we show how the set of occ solutions can be enumerated with O(log) delay after O(m)-time preprocessing of P. To achieve this, we develop an improved linear-space distance-sensitive weighted ancestor data structure.

It was recently proved that any straight-line program (SLP) generating a given string can be transformed in linear time into an equivalent balanced SLP of the same asymptotic size. We generalize this proof to a general class of grammars we call generalized SLPs (GSLPs), which allow rules of the form A → x where x is any Turing-complete representation (of size |x|) of a sequence of symbols (potentially much longer than |x|). We then specialize GSLPs to so-called Iterated SLPs (ISLPs), which allow rules of the form A →Π _i=k_1^k_2 B_1^i^c_1⋯ B_t^i^c_t of size 𝒪(t) . We prove that ISLPs break, for some text families, the measure δ based on substring complexity, a lower bound for most measures and compressors exploiting repetitiveness. Further, ISLPs can extract any substring of length λ , from the represented text T[1. .n] , in time 𝒪(λ + log ^2 nloglog n) . This is the first compressed representation for repetitive texts breaking δ while, at the same time, supporting direct access to arbitrary text symbols in polylogarithmic time. We also show how to compute some substring queries, like range minima and next/previous smaller value, in time 𝒪(log ^2 n loglog n) . Finally, we further specialize the grammars to run-length SLPs (RLSLPs), which restrict the rules allowed by ISLPs to the form A → B^t . Apart from inheriting all the previous results with the term log ^2 n loglog n reduced to the near-optimal log n , we show that RLSLPs can exploit balancedness to efficiently compute a wide class of substring queries we call “composable”—i.e., f(X · Y) can be obtained from f(X) and f(Y). As an example, we show how to compute Karp-Rabin fingerprints of texts substrings in 𝒪(log n) time. While the results on RLSLPs were already known, ours are much simpler and require little precomputation time and extra data associated with the grammar.

Suffixient sets are a novel prefix array (PA) compression technique based on subsampling PA (rather than compressing the entire array like previous techniques used to do): by storing very few entries of PA (in fact, a compressed number of entries), one can prove that pattern matching via binary search is still possible provided that random access is available on the text. In this paper, we tackle the problems of determining whether a given subset of text positions is (1) a suffixient set or (2) a suffixient set of minimum cardinality. We provide linear-time algorithms solving these problems.

Let T ∈ ^n be a text over alphabet . A suffixient set 𝒮⊆ [n] for T is a set of positions such that, for every one-character right-extension T[i, j] of every right-maximal substring T[i,j-1] of T, there exists x∈ S such that T[i, j] is a suffix of T[1, x]. It was recently shown that, given a suffixient set of cardinality q and an oracle offering fast random access on T (for example, a straight-line program), there is a data structure of O(q) words (on top of the oracle) that can quickly find all Maximal Exact Matches (MEMs) of any query pattern P in T with high probability. The paper introducing suffixient sets left open the problem of computing the smallest such set; in this paper, we solve this problem by describing a simple quadratic-time algorithm, a O(n + r̅| |) -time algorithm running in compressed working space ( r̅ is the number of runs in the Burrows-Wheeler transform of T reversed), and an optimal O(n)-time algorithm computing the smallest suffixient set. We present an implementation of our compressed-space algorithm and show experimentally that it uses a small memory footprint on repetitive text collections.

Two recent lower bounds on the compressibility of repetitive sequences, δ≤γ, have received much attention. It has been shown that a length-n string S over an alphabet of size σ can be represented within the optimal O(δlognlogσδlog n) space, and further, that within that space one can find all the occ occurrences in S of any length-m pattern in time O(mlog n + occ log^ϵ n) for any constant ϵ>0. Instead, the near-optimal search time O(m+(occ+1)log^ϵ n) has been achieved only within O(γlogn/γ) space. Both results are based on considerably different locally consistent parsing techniques. The question of whether the better search time could be supported within the δ-optimal space remained open. In this paper, we prove that both techniques can indeed be combined to obtain the best of both worlds: O(m+(occ+1)log^ϵ n) search time within O(δlognlogσδlog n) space. Moreover, the number of occurrences can be computed in O(m+log^2+ϵn) time within O(δlognlogσδlog n) space. We also show that an extra sublogarithmic factor on top of this space enables optimal O(m+occ) search time, whereas an extra logarithmic factor enables optimal O(m) counting time.

Wheeler nondeterministic finite automata (WNFAs) were introduced as a generalization of prefix sorting from strings to labeled graphs. WNFAs admit optimal solutions to classic hard problems on labeled graphs and languages. The problem of deciding whether a given NFA is Wheeler is known to be NP-complete. Recently, however, Alanko et al. showed how to side-step this complexity by switching to preorders: letting $Q$ be the set of states, $E$ the set of transitions, $|Q|=n$, and $|E|=m$, they provided a $O(mn^2)$-time algorithm computing a totally-ordered partition of the WNFA's states such that (1) equivalent states recognize the same regular language, and (2) the order of non-equivalent states is consistent with any Wheeler order, when one exists. Then, the output is a preorder of the states as useful for pattern matching as standard Wheeler orders. Further research generalized these concepts to arbitrary NFAs by introducing co-lex partial preorders: any NFA admits a partial preorder of its states reflecting the co-lex order of their accepted strings; the smaller the width of such preorder is, the faster regular expression matching queries can be performed. To date, the fastest algorithm for computing the smallest-width partial preorder on NFAs runs in $O(m^2+n^{5/2})$ time, while on DFAs the same can be done in $O(\min(n^2\log n,mn))$ time. In this paper, we provide much more efficient solutions to the problem above. Our results are achieved by extending a classic algorithm for the relational coarsest partition refinement problem to work with ordered partitions. Specifically, we provide a $O(m\log n)$-time algorithm computing a co-lex total preorder when the input is a WNFA, and an algorithm with the same time complexity computing the smallest-width co-lex partial order of any DFA. Also, we present implementations of our algorithms and show that they are very efficient in practice.

Sorting is a fundamental algorithmic pre-processing technique which often allows to represent data more compactly and, at the same time, speeds up search queries on it. In this paper, we focus on the well-studied problem of sorting and indexing string sets. Since the introduction of suffix trees in 1973, dozens of suffix sorting algorithms have been described in the literature. In 2017, these techniques were extended to sets of strings described by means of finite automata: the theory of Wheeler graphs [Gagie et al., TCS'17] introduced automata whose states can be totally-sorted according to the co-lexicographic (co-lex in the following) order of the prefixes of words accepted by the automaton. More recently, in [Cotumaccio, Prezza, SODA'21] it was shown how to extend these ideas to arbitrary automata by means of partial co-lex orders. This work showed that a co-lex order of minimum width (thus optimizing search query times) on deterministic finite automata (DFAs) can be computed in $O(m^2 + n^{5/2})$ time, $m$ being the number of transitions and $n$ the number of states of the input DFA. In this paper, we exhibit new combinatorial properties of the minimum-width co-lex order of DFAs and exploit them to design faster prefix sorting algorithms. In particular, we describe two algorithms sorting arbitrary DFAs in $O(mn)$ and $O(n^2\log n)$ time, respectively, and an algorithm sorting acyclic DFAs in $O(m\log n)$ time. Within these running times, all algorithms compute also a smallest chain partition of the partial order (required to index the DFA). We present an experiment result to show that an optimized implementation of the $O(n^2\log n)$-time algorithm exhibits a nearly-linear behaviour on large deterministic pan-genomic graphs and is thus also of practical interest.

It was recently proved that any SLP generating a given string w can be transformed in linear time into an equivalent balanced SLP of the same asymptotic size. We show that this result also holds for RLSLPs, which are SLPs extended with run-length rules of the form $$A \rightarrow B^t$$ for $$t>2$$ , deriving $$\texttt {exp}(A) = \texttt {exp}(B)^t$$ . An immediate consequence is the simplification of the algorithm for extracting substrings of an RLSLP-compressed string. We also show that several problems like answering RMQs and computing Karp-Rabin fingerprints on substrings can be solved in $$\mathcal {O}(g_{rl})$$ space and $$\mathcal {O}(\log n)$$ time, $$g_{rl}$$ being the size of the smallest RLSLP generating the string, of length n. We extend the result to solving more general operations on string ranges, in $$\mathcal {O}(g_{rl})$$ space and $$\mathcal {O}(\log n)$$ applications of the operation. In general, the smallest RLSLP can be asymptotically smaller than the smallest SLP by up to an $$\mathcal {O}(\log n)$$ factor, so our results can make a difference in terms of the space needed for computing these operations efficiently for some string families.

Two recent lower bounds on the compressiblity of repetitive sequences, δ ≤ γ , have received much attention. It has been shown that a string S [1.. n ] can be represented within the optimal O ( δ log n δ ) space, and further, that within that space one can find all the occ occurrences in S of any pattern of length m in time O ( m log n + o c c log ϵ n ) for any constant ϵ > 0 . Instead, the near-optimal search time O ( m + ( o c c + 1 ) log ϵ n ) was achieved only within O ( γ log n γ ) space. Both results are based on considerably different locally consistent parsing techniques. The question of whether the better search time could be obtained within the δ -optimal space was open. In this paper, we prove that both techniques can indeed be combined in order to obtain the best of both worlds, O ( m + ( o c c + 1 ) log ϵ n ) search time within O ( δ log n δ ) space.

Nowadays, the magnitude of the size of the data to be handled has grown widely. In many areas, it is necessary to work with data sets that are too big to fit in main memory. This situation has led to an increasing interest in efficiently compressing the data. In many situations, just compressing is not enough. It is also necessary to represent data in a way that can be queried without needing to decompress it. Among others, a compression method that permits working in compressed form is grammar compression. By using grammar compression, exponential compression rates can be achieved. On the other hand, direct access can take logarithmic time. Unlike other compression methods, grammar compression can achieve larger space reductions in either, repetitive and non-repetitive data. Recently published papers have shown that grammar compression can be achieved efficiently through a locally consistent parsing, which refers to a parsing where identical elements are parsed identically, with the possible exception of their extremes. In this research, we propose to improve the knowledge and applications of grammar-based compression. We will study an efficient method of grammar compression constructed through locally consistent parsing. Additionally, the research will focus on improving queries over repetitive data while representing it by a grammar. Finally, we will research a grammar-based compression method to improve algorithmic efficiency on abstract data types.

At any given moment, we experience a perceptual scene as a single whole and yet we may distinguish a variety of objects within it. This phenomenon instantiates two properties of conscious perception: integration and differentiation. Integration is the property of experiencing a collection of objects as a unitary percept and differentiation is the property of experiencing these objects as distinct from each other. Here, we evaluated the neural information dynamics underlying integration and differentiation of perceptual contents during bistable perception. Participants listened to a sequence of tones (auditory bistable stimuli) experienced either as a single stream (perceptual integration) or as two parallel streams (perceptual differentiation) of sounds. We computed neurophysiological indices of information integration and information differentiation with electroencephalographic and intracranial recordings. When perceptual alternations were endogenously driven, the integrated percept was associated with an increase in neural information integration and a decrease in neural differentiation across frontoparietal regions, whereas the opposite pattern was observed for the differentiated percept. However, when perception was exogenously driven by a change in the sound stream (no bistability), neural oscillatory power distinguished between percepts but information measures did not. We demonstrate that perceptual integration and differentiation can be mapped to theoretically motivated neural information signatures, suggesting a direct relationship between phenomenology and neurophysiology.

Cardiac fibroblasts (CF) are key cells for maintaining extracellular matrix (ECM) protein homeostasis in the heart, and for cardiac repair through CF-to-cardiac myofibroblast (CMF) differentiation. Additionally, CF play an important role in the inflammatory process after cardiac injury, and they express Toll like receptor 4 (TLR4), B1 and B2 bradykinin receptors (B1R and B2R) which are important in the inflammatory response. B1R and B2R are induced by proinflammatory cytokines and their activation by bradykinin (BK: B2R agonist) or des-arg-kallidin (DAKD: B1R agonist), induces NO and PGI2 production which is key for reducing collagen I levels. However, whether TLR4 activation regulates bradykinin receptor expression remains unknown. CF were isolated from human, neonatal rat and adult mouse heart. B1R mRNA expression was evaluated by qRT-PCR, whereas B1R, collagen, COX-2 and iNOS protein levels were evaluated by Western Blot. NO and PGI2 were evaluated by commercial kits. We report here that in CF, TLR4 activation increased B1R mRNA and protein levels, as well as COX-2 and iNOS levels. B1R mRNA levels were also induced by interleukin-1α via its cognate receptor IL-1R1. In LPS-pretreated CF the DAKD treatment induced higher responses with respect to those observed in non LPS-pretreated CF, increasing PGI2 secretion and NO production; and reducing collagen I protein levels in CF. In conclusion, no significant response to DAKD was observed (due to very low expression of B1R in CF) - but pre-activation of TLR4 in CF, conditions that significantly enhanced B1R expression, led to an additional response of DAKD.

In the study of consciousness, neurophenomenology was originally established as a novel research program attempting to reconcile two apparently irreconcilable methodologies in psychology: qualitative and quantitative methods. Its potential relies on Francisco Varela’s idea of reciprocal constraints, in which first-person accounts and neurophysiological data mutually inform each other. However, since its first conceptualization, neurophenomenology has encountered methodological problems. These problems have emerged mainly because of the difficulty of obtaining and analyzing subjective reports in a systematic manner. However, more recently, several interview techniques for describing subjective accounts have been developed, collectively known as “second-person methods.” Second-person methods refer to interview techniques that solicit both verbal and non-verbal information from participants in order to obtain systematic and detailed subjective reports. Here, we examine the potential for employing second-person methodologies in the neurophenomenological study of consciousness and we propose three practical ideas for developing a second-person neurophenomenological method. Thus, we first describe second-person methodologies available in the literature for analyzing subjective reports, identifying specific constraints on the status of the first-, second- and third- person methods. Second, we analyze two experimental studies that explicitly incorporate second-person methods for traversing the “gap” between phenomenology and neuroscience. Third, we analyze the challenges that second-person accounts face in establishing an objective methodology for comparing results across different participants and interviewers: this is the “validation” problem. Finally, we synthesize the common aspects of the interview methods described above. In conclusion, our arguments emphasize that second-person methods represent a powerful approach for closing the gap between the experiential and the neurobiological levels of description in the study of human consciousness.

Event Abstract Back to Event Induced gamma-band activity signals awareness of change in a bistable percept during wakefulness but changes dynamics with sleep onset. Andrés Canales-Johnson1, 2*, Daniela Cabezas3, Carolina Silva3, Francisco Olivares3, Roberto García3, Álvaro Rivera-Rei4, Valdas Noreika5, David Huepe4, Robert Carlyon5 and Tristan Bekinschtein5 1 Medical Research Council, United Kingdom 2 Laboratory of Cognitve and Social Neuroscience, Faculty of Psychology, Chile 3 Universidad Diego Portales, Chile 4 Universidad Diego Portales, Laboratory of Cognitive and Social Neuroscience, Chile 5 MRC Cognition and Brain Sciences Unit, United Kingdom Several neural markers of consciousness have been extensively studied by modifying different parameters of the stimuli in order to make them accessible or inaccessible to conscious report. Here we show a new approach to study conscious awareness by manipulating the level of wakefulness instead of the properties of the stimuli. Specifically, we propose to study whether traces of awareness can still be detected while participants fall asleep during an auditory bistability task using an electrophysiological marker of awareness. When a pair of tones with frequencies A and B is presented in the sequence 'ABA-ABA-ABA' in a fast rate, listeners report two different and alternating streams: a galloping rhythm and a duplet rhythm. This auditory streaming is an example of the alternating binding processes known as bistable awareness. In this study, wake participants listening to this auditory streaming are instructed to detect spontaneous changes in percepts by pressing a button with their left or right hand, while transitioning towards sleep. Using both electroencephalography and direct cortical recordings we measured the induced oscillatory activity in two conditions: a condition where changes in the auditory stream were triggered externally by changing physical properties of sounds (exogenous condition) and a condition where stimuli were constant and the auditory stream alternated spontaneously (endogenous condition). Our preliminary results showed that wake participants exhibit a transient increase in the induced gamma oscillatory activity 500 milliseconds before the motor report in the endogenous but not during the exogenous condition. Here, we propose to use this increase in gamma band activity as a neural marker of conscious awareness on an internal perceptual change while falling asleep. We hypothesize that induced gamma band activity will be diminished during drowsiness and the time in between spontaneous endogenous perceptual changes will be come longer. Keywords: Electroencephalography, Sleep, gamma oscillations, Conscious awareness, intracranial recordings Conference: XII International Conference on Cognitive Neuroscience (ICON-XII), Brisbane, Queensland, Australia, 27 Jul - 31 Jul, 2014. Presentation Type: Poster Topic: Cognition and Executive Processes Citation: Canales-Johnson A, Cabezas D, Silva C, Olivares F, García R, Rivera-Rei Á, Noreika V, Huepe D, Carlyon R and Bekinschtein T (2015). Induced gamma-band activity signals awareness of change in a bistable percept during wakefulness but changes dynamics with sleep onset.. Conference Abstract: XII International Conference on Cognitive Neuroscience (ICON-XII). doi: 10.3389/conf.fnhum.2015.217.00062 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 19 Feb 2015; Published Online: 24 Apr 2015. * Correspondence: Mr. Andrés Canales-Johnson, Medical Research Council, Cambridge, United Kingdom, andres.canales.j@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Andrés Canales-Johnson Daniela Cabezas Carolina Silva Francisco Olivares Roberto García Álvaro Rivera-Rei Valdas Noreika David Huepe Robert Carlyon Tristan Bekinschtein Google Andrés Canales-Johnson Daniela Cabezas Carolina Silva Francisco Olivares Roberto García Álvaro Rivera-Rei Valdas Noreika David Huepe Robert Carlyon Tristan Bekinschtein Google Scholar Andrés Canales-Johnson Daniela Cabezas Carolina Silva Francisco Olivares Roberto García Álvaro Rivera-Rei Valdas Noreika David Huepe Robert Carlyon Tristan Bekinschtein PubMed Andrés Canales-Johnson Daniela Cabezas Carolina Silva Francisco Olivares Roberto García Álvaro Rivera-Rei Valdas Noreika David Huepe Robert Carlyon Tristan Bekinschtein Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Theoretical advances in the science of consciousness have proposed that it is concomitant with balanced cortical integration and differentiation, enabled by efficient networks of information transfer across multiple scales. Here, we apply graph theory to compare key signatures of such networks in high-density electroencephalographic data from 32 patients with chronic disorders of consciousness, against normative data from healthy controls. Based on connectivity within canonical frequency bands, we found that patient networks had reduced local and global efficiency, and fewer hubs in the alpha band. We devised a novel topographical metric, termed modular span, which showed that the alpha network modules in patients were also spatially circumscribed, lacking the structured long-distance interactions commonly observed in the healthy controls. Importantly however, these differences between graph-theoretic metrics were partially reversed in delta and theta band networks, which were also significantly more similar to each other in patients than controls. Going further, we found that metrics of alpha network efficiency also correlated with the degree of behavioural awareness. Intriguingly, some patients in behaviourally unresponsive vegetative states who demonstrated evidence of covert awareness with functional neuroimaging stood out from this trend: they had alpha networks that were remarkably well preserved and similar to those observed in the controls. Taken together, our findings inform current understanding of disorders of consciousness by highlighting the distinctive brain networks that characterise them. In the significant minority of vegetative patients who follow commands in neuroimaging tests, they point to putative network mechanisms that could support cognitive function and consciousness despite profound behavioural impairment.