Non-linear Pattern Matching with Backtracking for Non-free Data Types

Non-free data types are data types whose data have no canonical forms. For example, multisets are non-free data types because the multiset $\{a,b,b\}$ has two other equivalent but literally different forms $\{b,a,b\}$ and $\{b,b,a\}$. Pattern matching is known to provide a handy tool set to treat such data types. Although many studies on pattern matching and implementations for practical programming languages have been proposed so far, we observe that none of these studies satisfy all the criteria of practical pattern matching, which are as follows: i) efficiency of the backtracking algorithm for non-linear patterns, ii) extensibility of matching process, and iii) polymorphism in patterns. This paper aims to design a new pattern-matching-oriented programming language that satisfies all the above three criteria. The proposed language features clean Scheme-like syntax and efficient and extensible pattern matching semantics. This programming language is especially useful for the processing of complex non-free data types that not only include multisets and sets but also graphs and symbolic mathematical expressions. We discuss the importance of our criteria of practical pattern matching and how our language design naturally arises from the criteria. The proposed language has been already implemented and open-sourced as the Egison programming language

Methodology to quantify clogging coefficients for grated inlets: application to SANT MARTI catchment (Barcelona)

This is the accepted version of the following article: Gómez, M, Parés, J, Russo, B, Martínez‐Gomariz, E. Methodology to quantify clogging coefficients for grated inlets. Application to SANT MARTI catchment (Barcelona). J Flood Risk Management. 2019; 12:e12479. https://doi.org/10.1111/jfr3.12479, which has been published in final form at https://onlinelibrary.wiley.com/doi/10.1111/jfr3.12479.Within the drainage system of a city, the set of inlets is in charge of taking the runoff produced by local storms to the stormwater/sewer. In the drainage system design the selection of appropriate inlet models and their location is one of the fundamental aspects. The hydraulics of these inlets has received great attention within the last years; however, few inlet makers provide the hydraulic capacity of their products. In addition, these data usually consider clean water, while in reality, numerous inlets can be either totally or partially clogged. This aspect should be kept in mind within the design process. In this paper, a methodology to consider the hydraulic effects of clogging phenomena is presented. The work started from a visual inspection of the grated inlets throughout the urban catchment of Sant Martí, Barcelona, as a means of identifying clogging patterns, their repetitive forms and their associated frequency. After that, clogged patterns were reproduced in laboratory testing of typical inlets types, thereby obtaining the real quantity of water that could be captured by each of them. It was shown that the same expression employed to describe the efficiency of clean inlets can be used to assess the efficiency of those clogged. A reduction factor in terms of hydraulic capacity and related to each clogging pattern has been defined for use in hydraulic studies of runoff along streets. Finally, the paper compares the obtained results in terms of clogging coefficient with another experimental campaign carried out in other catchment of the city.Peer ReviewedPostprint (author's final draft

Trajectory-based differential expression analysis for single-cell sequencing data

Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression. Downstream of trajectory inference, it is vital to discover genes that are (i) associated with the lineages in the trajectory, or (ii) differentially expressed between lineages, to illuminate the underlying biological processes. Current data analysis procedures, however, either fail to exploit the continuous resolution provided by trajectory inference, or fail to pinpoint the exact types of differential expression. We introduce tradeSeq, a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of both within-lineage and between-lineage differential expression. By incorporating observation-level weights, the model additionally allows to account for zero inflation. We evaluate the method on simulated datasets and on real datasets from droplet-based and full-length protocols, and show that it yields biological insights through a clear interpretation of the data. Downstream of trajectory inference for cell lineages based on scRNA-seq data, differential expression analysis yields insight into biological processes. Here, Van den Berge et al. develop tradeSeq, a framework for the inference of within and between-lineage differential expression, based on negative binomial generalized additive models