Recognition of pen-based music notation with finite-state machines

This work presents a statistical model to recognize pen-based music compositions using stroke recognition algorithms and finite-state machines. The series of strokes received as input is mapped onto a stochastic representation, which is combined with a formal language that describes musical symbols in terms of stroke primitives. Then, a Probabilistic Finite-State Automaton is obtained, which defines probabilities over the set of musical sequences. This model is eventually crossed with a semantic language to avoid sequences that does not make musical sense. Finally, a decoding strategy is applied in order to output a hypothesis about the musical sequence actually written. Comprehensive experimentation with several decoding algorithms, stroke similarity measures and probability density estimators are tested and evaluated following different metrics of interest. Results found have shown the goodness of the proposed model, obtaining competitive performances in all metrics and scenarios considered.This work was supported by the Spanish Ministerio de Educación, Cultura y Deporte through a FPU Fellowship (Ref. AP2012–0939) and the Spanish Ministerio de Economía y Competitividad through the TIMuL Project (No. TIN2013-48152-C2-1-R, supported by UE FEDER funds)

Acute Kidney Injury in the Intensive Care Unit

Acute kidney injury (AKI) is defined as an abrupt decrease in glomerular filtration rate (GFR). Incidence varies from 20% to as high as 70% in critically ill patients. Classically, AKI has been divided into three broad pathophysiologic categories: prerenal AKI, intrinsic AKI, and postrenal (obstructive) AKI. The clinical manifestations of AKI vary among a wide range of symptoms and metabolic abnormalities. A sudden decrease in GFR will result in rising concentrations of solutes in the blood, which are normally excreted by the kidneys. Recently, new urinary and serum biomarkers have gained a place in the diagnosis, classification, and prognosis prediction of AKI. The best treatment for AKI is prevention. Patients with prerenal azotemia should have intravascular volume deficits corrected and cardiac function optimized. Obstructive (postrenal) kidney disease is treated by mechanical relief of the block. The primary management of acute interstitial nephritis is discontinuation of the inciting agent. Renal replacement therapy (RRT) has emerged as a supportive mechanism rather than just as a lifesaving measure. Continuous techniques are preferable in treating critically ill patients, although every modality has its benefits, indications, and contraindications

Recognition of online handwritten music symbols

Paper submitted to MML 2013, 6th International Workshop on Machine Learning and Music, Prague, September 23, 2013.An effective way of digitizing a new musical composition is to use an e-pen and tablet application in which the user's pen strokes are recognized online and the digital score is created with the sole effort of the composition itself. This work aims to be a starting point for research on the recognition of online handwritten music notation. To this end, different alternatives within the two modalities of recognition resulting from this data are presented: online recognition, which uses the strokes marked by a pen, and offline recognition, which uses the image generated after drawing the symbol. A comparative experiment with common machine learning algorithms over a dataset of 3800 samples and 32 different music symbols is presented. Results show that samples of the actual user are needed if good classification rates are pursued. Moreover, algorithms using the online data, on average, achieve better classification results than the others

A framework for efficient execution of matrix computations

Matrix computations lie at the heart of most scientific computational tasks. The solution of linear systems of equations is a very frequent operation in many fields in science, engineering, surveying, physics and others. Other matrix operations occur frequently in many other fields such as pattern recognition and classification, or multimedia applications. Therefore, it is important to perform matrix operations efficiently. The work in this thesis focuses on the efficient execution on commodity processors of matrix operations which arise frequently in different fields.We study some important operations which appear in the solution of real world problems: some sparse and dense linear algebra codes and a classification algorithm. In particular, we focus our attention on the efficient execution of the following operations: sparse Cholesky factorization; dense matrix multiplication; dense Cholesky factorization; and Nearest Neighbor Classification.A lot of research has been conducted on the efficient parallelization of numerical algorithms. However, the efficiency of a parallel algorithm depends ultimately on the performance obtained from the computations performed on each node. The work presented in this thesis focuses on the sequential execution on a single processor.There exists a number of data structures for sparse computations which can be used in order to avoid the storage of and computation on zero elements. We work with a hierarchical data structure known as hypermatrix. A matrix is subdivided recursively an arbitrary number of times. Several pointer matrices are used to store the location ofsubmatrices at each level. The last level consists of data submatrices which are dealt with as dense submatrices. When the block size of this dense submatrices is small, the number of zeros can be greatly reduced. However, the performance obtained from BLAS3 routines drops heavily. Consequently, there is a trade-off in the size of data submatrices used for a sparse Cholesky factorization with the hypermatrix scheme. Our goal is that of reducing the overhead introduced by the unnecessary operation on zeros when a hypermatrix data structure is used to produce a sparse Cholesky factorization. In this work we study several techniques for reducing such overhead in order to obtain high performance.One of our goals is the creation of codes which work efficiently on different platforms when operating on dense matrices. To obtain high performance, the resources offered by the CPU must be properly utilized. At the same time, the memory hierarchy must be exploited to tolerate increasing memory latencies. To achieve the former, we produce inner kernels which use the CPU very efficiently. To achieve the latter, we investigate nonlinear data layouts. Such data formats can contribute to the effective use of the memory system.The use of highly optimized inner kernels is of paramount importance for obtaining efficient numerical algorithms. Often, such kernels are created by hand. However, we want to create efficient inner kernels for a variety of processors using a general approach and avoiding hand-made codification in assembly language. In this work, we present an alternative way to produce efficient kernels automatically, based on a set of simple codes written in a high level language, which can be parameterized at compilation time. The advantage of our method lies in the ability to generate very efficient inner kernels by means of a good compiler. Working on regular codes for small matrices most of the compilers we used in different platforms were creating very efficient inner kernels for matrix multiplication. Using the resulting kernels we have been able to produce high performance sparse and dense linear algebra codes on a variety of platforms.In this work we also show that techniques used in linear algebra codes can be useful in other fields. We present the work we have done in the optimization of the Nearest Neighbor classification focusing on the speed of the classification process.Tuning several codes for different problems and machines can become a heavy and unbearable task. For this reason we have developed an environment for development and automatic benchmarking of codes which is presented in this thesis.As a practical result of this work, we have been able to create efficient codes for several matrix operations on a variety of platforms. Our codes are highly competitive with other state-of-art codes for some problems

Extensions to rank-based prototype selection in k-Nearest Neighbour classification

The k-nearest neighbour rule is commonly considered for classification tasks given its straightforward implementation and good performance in many applications. However, its efficiency represents an obstacle in real-case scenarios because the classification requires computing a distance to every single prototype of the training set. Prototype Selection (PS) is a typical approach to alleviate this problem, which focuses on reducing the size of the training set by selecting the most interesting prototypes. In this context, rank methods have been postulated as a good solution: following some heuristics, these methods perform an ordering of the prototypes according to their relevance in the classification task, which is then used to select the most relevant ones. This work presents a significant improvement of existing rank methods by proposing two extensions: (i) a greater robustness against noise at label level by considering the parameter ‘k’ of the classification in the selection process; and (ii) a new parameter-free rule to select the prototypes once they have been ordered. The experiments performed in different scenarios and datasets demonstrate the goodness of these extensions. Also, it is empirically proved that the new full approach is competitive with respect to existing PS algorithms.This work is supported by the Spanish Ministry HISPAMUS project TIN2017-86576-R, partially funded by the EU

An efficient approach for Interactive Sequential Pattern Recognition

Interactive Pattern Recognition (IPR) is an emergent framework in which the user is involved actively in the recognition process by giving feedback to the system when an error is detected. Although this framework is expected to reduce the number of errors to correct, it may increase the time required to complete the task since the machine needs to recompute its proposal after each interaction. Therefore, a fast computation is required to make the interactive system profitable and user-friendly. This work presents an efficient approach to deal with IPR tasks when data has a sequential nature. Our approach includes some computation at the very beginning of the task but it then achieves a linear complexity after user corrections. We also show how these tasks can be effectively carried out if the solution space is defined with a Regular Language. This fact has indeed proven to be the most relevant factor to improve the efficiency of the approach. Several experiments are carried out in which our proposal is faced against a classical search. Results show a reduction in time in all experiments considered, solving efficiently some complex IPR tasks thanks to our proposals.This work was partially supported by the Spanish Ministerio de Educación, Cultura y Deporte through FPU fellowship (AP2012-0939) and the Spanish Ministerio de Economía y Competitividad through Project TIMuL (No. TIN2013-48152-C2-1-R, supported by UE FEDER funds)

Improving kNN multi-label classification in Prototype Selection scenarios using class proposals

Prototype Selection (PS) algorithms allow a faster Nearest Neighbor classification by keeping only the most profitable prototypes of the training set. In turn, these schemes typically lower the performance accuracy. In this work a new strategy for multi-label classifications tasks is proposed to solve this accuracy drop without the need of using all the training set. For that, given a new instance, the PS algorithm is used as a fast recommender system which retrieves the most likely classes. Then, the actual classification is performed only considering the prototypes from the initial training set belonging to the suggested classes. Results show that this strategy provides a large set of trade-off solutions which fills the gap between PS-based classification efficiency and conventional kNN accuracy. Furthermore, this scheme is not only able to, at best, reach the performance of conventional kNN with barely a third of distances computed, but it does also outperform the latter in noisy scenarios, proving to be a much more robust approach.This work was partially supported by the Spanish Ministerio de Educación, Cultura y Deporte through FPU Fellowship (AP2012–0939), the Spanish Ministerio de Economía y Competitividad through Project TIMuL (TIN2013-48152-C2-1-R), Consejería de Educación de la Comunidad Valenciana through Project PROMETEO/2012/017 and Vicerrectorado de Investigación, Desarrollo e Innovación de la Universidad de Alicante through FPU Program (UAFPU2014–5883)

Effects of Exogenous Yeast and Bacteria on the Microbial Population Dynamics and Outcomes of Olive Fermentations.

In this study, we examined Sicilian-style green olive fermentations upon the addition of Saccharomyces cerevisiae UCDFST 09-448 and/or Pichia kudriazevii UCDFST09-427 or the lactic acid bacteria (LAB) Lactobacillus plantarum AJ11R and Leuconostoc pseudomesenteroides BGM3R. Olives containing S. cerevisiae UCDFST 09-448, a strain able to hydrolyze pectin, but not P. kudriazevii UCDFST 09-427, a nonpectinolytic strain, exhibited excessive tissue damage within 4 weeks. DNA sequencing of fungal internal transcribed spacer (ITS) regions and comparisons to a yeast-specific ITS sequence database remarkably showed that neither S. cerevisiae UCDFST 09-448 nor P. kudriazevii UCDFST 09-427 resulted in significant changes to yeast species diversity. Instead, Candida boidinii constituted the majority (>90%) of the total yeast present, independent of whether S. cerevisiae or P. kudriazevii was added. By comparison, Lactobacillus species were enriched in olives inoculated with potential starter LAB L. plantarum AJ11R and L. pseudomesenteroides BGM3R according to community 16S rRNA gene sequence analysis. The bacterial diversity of those olives was significantly reduced and resembled control fermentations incubated for a longer period of time. Importantly, microbial populations were highly dynamic at the strain level, as indicated by the large variations in AJ11R and BGM3R cell numbers over time and reductions in the numbers of yeast isolates expressing polygalacturonase activity. These findings show the distinct effects of exogenous spoilage and starter microbes on indigenous communities in plant-based food fermentations that result in very different impacts on product quality. IMPORTANCE Food fermentations are subject to tremendous selective pressures resulting in the growth and persistence of a limited number of bacterial and fungal taxa. Although these foods are vulnerable to spoilage by unintended contamination of certain microorganisms, or alternatively, can be improved by the deliberate addition of starter culture microbes that accelerate or beneficially modify product outcomes, the impact of either of those microbial additions on community dynamics within the fermentations is not well understood at strain-specific or global scales. Herein, we show how exogenous spoilage yeast or starter lactic acid bacteria confer very different effects on microbial numbers and diversity in olive fermentations. Introduced microbes have long-lasting consequences and result in changes that are apparent even when levels of those inoculants and their major enzymatic activities decline. This work has direct implications for understanding bacterial and fungal invasions of microbial habitats resulting in pivotal changes to community structure and function

Late multimodal fusion for image and audio music transcription

Music transcription, which deals with the conversion of music sources into a structured digital format, is a key problem for Music Information Retrieval (MIR). When addressing this challenge in computational terms, the MIR community follows two lines of research: music documents, which is the case of Optical Music Recognition (OMR), or audio recordings, which is the case of Automatic Music Transcription (AMT). The different nature of the aforementioned input data has conditioned these fields to develop modality-specific frameworks. However, their recent definition in terms of sequence labeling tasks leads to a common output representation, which enables research on a combined paradigm. In this respect, multimodal image and audio music transcription comprises the challenge of effectively combining the information conveyed by image and audio modalities. In this work, we explore this question at a late-fusion level: we study four combination approaches in order to merge, for the first time, the hypotheses regarding end-to-end OMR and AMT systems in a lattice-based search space. The results obtained for a series of performance scenarios–in which the corresponding single-modality models yield different error rates–showed interesting benefits of these approaches. In addition, two of the four strategies considered significantly improve the corresponding unimodal standard recognition frameworks.This paper is part of the I+D+i PID2020-118447RA-I00 (MultiScore) project, funded by MCIN/AEI/10.13039/501100011033. Some of the computing resources were provided by the Generalitat Valenciana and the European Union through the FEDER funding programme (IDIFEDER/2020/003). The first and second authors are respectively supported by grants FPU19/04957 from the Spanish Ministerio de Universidades and APOSTD/2020/256 from Generalitat Valenciana

Few-Shot Symbol Classification via Self-Supervised Learning and Nearest Neighbor

The recognition of symbols within document images is one of the most relevant steps involved in the Document Analysis field. While current state-of-the-art methods based on Deep Learning are capable of adequately performing this task, they generally require a vast amount of data that has to be manually labeled. In this paper, we propose a self-supervised learning-based method that addresses this task by training a neural-based feature extractor with a set of unlabeled documents and performs the recognition task considering just a few reference samples. Experiments on different corpora comprising music, text, and symbol documents report that the proposal is capable of adequately tackling the task with high accuracy rates of up to 95% in few-shot settings. Moreover, results show that the presented strategy outperforms the base supervised learning approaches trained with the same amount of data that, in some cases, even fail to converge. This approach, hence, stands as a lightweight alternative to deal with symbol classification with few annotated data.This paper is part of the project I+D+i PID2020-118447RA-I00 (MultiScore), funded by MCIN/AEI/10.13039/501100011033. The first author is supported by grant FPU19/04957 from the Spanish Ministerio de Universidades. The second and third authors are respectively supported by grants ACIF/2021/356 and APOSTD/2020/256 from “Programa I+D+i de la Generalitat Valenciana”