arXiv subjects

Marco Previtali

Publications and source records attributed to Marco Previtali.

7 recordsLinked to original sources

arXiv2026

Granular thermostat implementation within the soft-sphere Discrete Element Method (DEM) framework, considerations and limitations

Dense granular flows are commonly investigated using the soft-sphere Discrete Element Method (DEM), whereas large-scale applications generally require continuum models. Granular temperature, defined as the variance of particle velocity fluctuations, is a relevant variable to collapse rheological scaling. However, experimental forcing approaches used to control temperature also affects other aspects of the granular state. An alternative is to use a thermostat algorithm to control the fluctuation energy directly. Although thermostat algorithms are well established in Molecular Dynamics (MD), their behaviour in dissipative DEM systems has received comparatively little attention. We show that conventional Langevin and Nosé-Hoover thermostats exhibit complementary limitations: Langevin control requires strong coupling to offset collisional dissipation, thereby damping particle dynamics, whereas Nosé-Hoover does not independently disrupt the correlations and segregation generated by repeated inelastic collisions, leading to non-ergodic and numerically unstable states. To address these limitations, we introduce two pairwise hybrid formulations combining deterministic temperature regulation with stochastic decorrelation. Both enforce the prescribed temperature, while variation of the stochastic decorrelation timescale modifies velocity statistics and associated mesoscopic properties such as diffusivity. Finally, application to pressure-controlled simple shear shows that increasing granular temperature at fixed inertial number reduces the apparent friction, consistent with previously reported trends. The framework therefore provides controlled reference states for comparing differently forced granular systems and identifying the variables required for temperature-dependent constitutive models.

cond-mat.soft↗

arXiv2022

Numeric Lyndon-based feature embedding of sequencing reads for machine learning approaches

Feature embedding methods have been proposed in literature to represent sequences as numeric vectors to be used in some bioinformatics investigations, such as family classification and protein structure prediction. Recent theoretical results showed that the well-known Lyndon factorization preserves common factors in overlapping strings. Surprisingly, the fingerprint of a sequencing read, which is the sequence of lengths of consecutive factors in variants of the Lyndon factorization of the read, is effective in preserving sequence similarities, suggesting it as basis for the definition of novels representations of sequencing reads. We propose a novel feature embedding method for Next-Generation Sequencing (NGS) data using the notion of fingerprint. We provide a theoretical and experimental framework to estimate the behaviour of fingerprints and of the $k$-mers extracted from it, called $k$-fingers, as possible feature embeddings for sequencing reads. As a case study to assess the effectiveness of such embeddings, we use fingerprints to represent RNA-Seq reads and to assign them to the most likely gene from which they were originated as fragments of transcripts of the gene. We provide an implementation of the proposed method in the tool lyn2vec, which produces Lyndon-based feature embeddings of sequencing reads.

q-bio.GN↗

arXiv2020

Computing the BWT and LCP array of a Set of Strings in External Memory

Indexing very large collections of strings, such as those produced by the widespread next generation sequencing technologies, heavily relies on multistring generalization of the Burrows-Wheeler Transform (BWT): large requirements of in-memory approaches have stimulated recent developments on external memory algorithms. The related problem of computing the Longest Common Prefix (LCP) array of a set of strings is instrumental to compute the suffix-prefix overlaps among strings, which is an essential step for many genome assembly algorithms. In a previous paper, we presented an in-memory divide-and-conquer method for building the BWT and LCP where we merge partial BWTs with a forward approach to sort suffixes. In this paper, we propose an alternative backward strategy to develop an external memory method to simultaneously build the BWT and the LCP array on a collection of m strings of different lengths. The algorithm over a set of strings having constant length k has O(mkl) time and I/O volume, using O(k + m) main memory, where l is the maximum value in the LCP array.

cs.DS↗

arXiv2017

FSG: Fast String Graph Construction for De Novo Assembly of Reads Data

The string graph for a collection of next-generation reads is a lossless data representation that is fundamental for de novo assemblers based on the overlap-layout-consensus paradigm. In this paper, we explore a novel approach to compute the string graph, based on the FM-index and Burrows-Wheeler Transform. We describe a simple algorithm that uses only the FM-index representation of the collection of reads to construct the string graph, without accessing the input reads. Our algorithm has been integrated into the SGA assembler as a standalone module to construct the string graph. The new integrated assembler has been assessed on a standard benchmark, showing that FSG is significantly faster than SGA while maintaining a moderate use of main memory, and showing practical advantages in running FSG on multiple threads.

cs.DS↗

arXiv2016

A New Lightweight Algorithm to compute the BWT and the LCP array of a Set of Strings

Indexing of very large collections of strings such as those produced by the widespread sequencing technologies, heavily relies on multi-string generalizations of the Burrows-Wheeler Transform (BWT), and for this problem various in-memory algorithms have been proposed. The rapid growing of data that are processed routinely, such as in bioinformatics, requires a large amount of main memory, and this fact has motivated the development of algorithms, to compute the BWT, that work almost entirely in external memory. On the other hand, the related problem of computing the Longest Common Prefix (LCP) array is often instrumental in several algorithms on collection of strings, such as those that compute the suffix-prefix overlap among strings, which is an essential step for many genome assembly algorithms. The best current lightweight approach to compute BWT and LCP array on a set of $m$ strings, each one $k$ characters long, has I/O complexity that is $O(mk^2 \log |Σ|)$ (where $|Σ|$ is the size of the alphabet), thus it is not optimal. In this paper we propose a novel approach to build BWT and LCP array (simultaneously) with $O(kmL(\log k +\log σ))$ I/O complexity, where $L$ is the length of longest substring that appears at least twice in the input strings.

cs.DS↗

arXiv2016

Fully Dynamic de Bruijn Graphs

We present a space- and time-efficient fully dynamic implementation de Bruijn graphs, which can also support fixed-length jumbled pattern matching.

cs.DS↗

arXiv2015

An External-Memory Algorithm for String Graph Construction

Some recent results have introduced external-memory algorithms to compute self-indexes of a set of strings, mainly via computing the Burrows-Wheeler Transform (BWT) of the input strings. The motivations for those results stem from Bioinformatics, where a large number of short strings (called reads) are routinely produced and analyzed. In that field, a fundamental problem is to assemble a genome from a large set of much shorter samples extracted from the unknown genome. The approaches that are currently used to tackle this problem are memory-intensive. This fact does not bode well with the ongoing increase in the availability of genomic data. A data structure that is used in genome assembly is the string graph, where vertices correspond to samples and arcs represent two overlapping samples. In this paper we address an open problem: to design an external-memory algorithm to compute the string graph.

cs.DS↗