Sample-Parallel Execution of EBCOT in Fast Mode

JPEG 2000’s most computationally expensive building block is the Embedded Block Coder with Optimized Truncation (EBCOT). This paper evaluates how encoders targeting a parallel architecture such as a GPU can increase their throughput in use cases where very high data rates are used. The compression efficiency in the less significant bit-planes is then often poor and it is beneficial to enable the Selective Arithmetic Coding Bypass style (fast mode) in order to trade a small loss in compression efficiency for a reduction of the computational complexity. More importantly, this style exposes a more finely grained parallelism that can be exploited to execute the raw coding passes, including bit-stuffing, in a sample-parallel fashion. For a latency- or memory critical application that encodes one frame at a time, EBCOT’s tier-1 is sped up between 1.1x and 2.4x compared to an optimized GPU-based implementation. When a low GPU occupancy has already been addressed by encoding multiple frames in parallel, the throughput can still be improved by 5% for high-entropy images and 27% for low-entropy images. Best results are obtained when enabling the fast mode after the fourth significant bit-plane. For most of the test images the compression rate is within 1% of the original

Simulating Spiking Neural P systems without delays using GPUs

We present in this paper our work regarding simulating a type of P system known as a spiking neural P system (SNP system) using graphics processing units (GPUs). GPUs, because of their architectural optimization for parallel computations, are well-suited for highly parallelizable problems. Due to the advent of general purpose GPU computing in recent years, GPUs are not limited to graphics and video processing alone, but include computationally intensive scientific and mathematical applications as well. Moreover P systems, including SNP systems, are inherently and maximally parallel computing models whose inspirations are taken from the functioning and dynamics of a living cell. In particular, SNP systems try to give a modest but formal representation of a special type of cell known as the neuron and their interactions with one another. The nature of SNP systems allowed their representation as matrices, which is a crucial step in simulating them on highly parallel devices such as GPUs. The highly parallel nature of SNP systems necessitate the use of hardware intended for parallel computations. The simulation algorithms, design considerations, and implementation are presented. Finally, simulation results, observations, and analyses using an SNP system that generates all numbers in $\mathbb N$ - {1} are discussed, as well as recommendations for future work.Comment: 19 pages in total, 4 figures, listings/algorithms, submitted at the 9th Brainstorming Week in Membrane Computing, University of Seville, Spai

Evaluation of GPU/CPU Co-Processing Models for JPEG 2000 Packetization

With the bottom-line goal of increasing the throughput of a GPU-accelerated JPEG 2000 encoder, this paper evaluates whether the post-compression rate control and packetization routines should be carried out on the CPU or on the GPU. Three co-processing models that differ in how the workload is split among the CPU and GPU are introduced. Both routines are discussed and algorithms for executing them in parallel are presented. Experimental results for compressing a detail-rich UHD sequence to 4 bits/sample indicate speed-ups of 200x for the rate control and 100x for the packetization compared to the single-threaded implementation in the commercial Kakadu library. These two routines executed on the CPU take 4x as long as all remaining coding steps on the GPU and therefore present a bottleneck. Even if the CPU bottleneck could be avoided with multi-threading, it is still beneficial to execute all coding steps on the GPU as this minimizes the required device-to-host transfer and thereby speeds up the critical path from 17.2 fps to 19.5 fps for 4 bits/sample and to 22.4 fps for 0.16 bits/sample

Design and assessment of a computer-assisted artificial intelligence system for predicting preterm labor in women attending regular check-ups. Emphasis in imbalance data learning technique

Tesis por compendio[ES] El parto prematuro, definido como el nacimiento antes de las 37 semanas de gestación, es una importante preocupación mundial con implicaciones para la salud de los recién nacidos y los costes económicos. Afecta aproximadamente al 11% de todos los nacimientos, lo que supone más de 15 millones de individuos en todo el mundo. Los métodos actuales para predecir el parto prematuro carecen de precisión, lo que conduce a un sobrediagnóstico y a una viabilidad limitada en entornos clínicos. La electrohisterografía (EHG) ha surgido como una alternativa prometedora al proporcionar información relevante sobre la electrofisiología uterina. Sin embargo, los sistemas de predicción anteriores basados en EHG no se han trasladado de forma efectiva a la práctica clínica, debido principalmente a los sesgos en el manejo de datos desbalanceados y a la necesidad de modelos de predicción robustos y generalizables. Esta tesis doctoral pretende desarrollar un sistema de predicción del parto prematuro basado en inteligencia artificial utilizando EHG y datos obstétricos de mujeres sometidas a controles prenatales regulares. Este sistema implica la extracción de características relevantes, la optimización del subespacio de características y la evaluación de estrategias para abordar el reto de los datos desbalanceados para una predicción robusta. El estudio valida la eficacia de las características temporales, espectrales y no lineales para distinguir entre casos de parto prematuro y a término. Las nuevas medidas de entropía, en concreto la dispersión y la entropía de burbuja, superan a las métricas de entropía tradicionales en la identificación del parto prematuro. Además, el estudio trata de maximizar la información complementaria al tiempo que minimiza la redundancia y las características de ruido para optimizar el subespacio de características para una predicción precisa del parto prematuro mediante un algoritmo genético. Además, se ha confirmado la fuga de información entre el conjunto de datos de entrenamiento y el de prueba al generar muestras sintéticas antes de la partición de datos, lo que da lugar a una capacidad de generalización sobreestimada del sistema predictor. Estos resultados subrayan la importancia de particionar y después remuestrear para garantizar la independencia de los datos entre las muestras de entrenamiento y de prueba. Se propone combinar el algoritmo genético y el remuestreo en la misma iteración para hacer frente al desequilibrio en el aprendizaje de los datos mediante el enfoque de particio'n-remuestreo, logrando un área bajo la curva ROC del 94% y una precisión media del 84%. Además, el modelo demuestra un F1-score y una sensibilidad de aproximadamente el 80%, superando a los estudios existentes que consideran el enfoque de remuestreo después de particionar. Esto revela el potencial de un sistema de predicción de parto prematuro basado en EHG, permitiendo estrategias orientadas al paciente para mejorar la prevención del parto prematuro, el bienestar materno-fetal y la gestión óptima de los recursos hospitalarios. En general, esta tesis doctoral proporciona a los clínicos herramientas valiosas para la toma de decisiones en escenarios de riesgo materno-fetal de parto prematuro. Permite a los clínicos diseñar estrategias orientadas al paciente para mejorar la prevención y el manejo del parto prematuro. La metodología propuesta es prometedora para el desarrollo de un sistema integrado de predicción del parto prematuro que pueda mejorar la planificación del embarazo, optimizar la asignación de recursos y reducir el riesgo de parto prematuro.[CA] El part prematur, definit com el naixement abans de les 37 setmanes de gestacio', e's una important preocupacio' mundial amb implicacions per a la salut dels nounats i els costos econo¿mics. Afecta aproximadament a l'11% de tots els naixements, la qual cosa suposa me's de 15 milions d'individus a tot el mo'n. Els me¿todes actuals per a predir el part prematur manquen de precisio', la qual cosa condueix a un sobrediagno¿stic i a una viabilitat limitada en entorns cl¿'nics. La electrohisterografia (EHG) ha sorgit com una alternativa prometedora en proporcionar informacio' rellevant sobre l'electrofisiologia uterina. No obstant aixo¿, els sistemes de prediccio' anteriors basats en EHG no s'han traslladat de manera efectiva a la pra¿ctica cl¿'nica, degut principalment als biaixos en el maneig de dades desequilibrades i a la necessitat de models de prediccio' robustos i generalitzables. Aquesta tesi doctoral prete'n desenvolupar un sistema de prediccio' del part prematur basat en intel·lige¿ncia artificial utilitzant EHG i dades obste¿triques de dones sotmeses a controls prenatals regulars. Aquest sistema implica l'extraccio' de caracter¿'stiques rellevants, l'optimitzacio' del subespai de caracter¿'stiques i l'avaluacio' d'estrate¿gies per a abordar el repte de les dades desequilibrades per a una prediccio' robusta. L'estudi valguda l'efica¿cia de les caracter¿'stiques temporals, espectrals i no lineals per a distingir entre casos de part prematur i a terme. Les noves mesures d'entropia, en concret la dispersio' i l'entropia de bambolla, superen a les me¿triques d'entropia tradicionals en la identificacio' del part prematur. A me's, l'estudi tracta de maximitzar la informacio' complementa¿ria al mateix temps que minimitza la redunda¿ncia i les caracter¿'stiques de soroll per a optimitzar el subespai de caracter¿'stiques per a una prediccio' precisa del part prematur mitjan¿cant un algorisme gene¿tic. A me's, hem confirmat la fugida d'informacio' entre el conjunt de dades d'entrenament i el de prova en generar mostres sinte¿tiques abans de la particio' de dades, la qual cosa dona lloc a una capacitat de generalitzacio' sobreestimada del sistema predictor. Aquests resultats subratllen la importa¿ncia de particionar i despre's remostrejar per a garantir la independe¿ncia de les dades entre les mostres d'entrenament i de prova. Proposem combinar l'algorisme gene¿tic i el remostreig en la mateixa iteracio' per a fer front al desequilibri en l'aprenentatge de les dades mitjan¿cant l'enfocament de particio'-remostrege, aconseguint una a¿rea sota la corba ROC del 94% i una precisio' mitjana del 84%. A me's, el model demostra una puntuacio' F1 i una sensibilitat d'aproximadament el 80%, superant als estudis existents que consideren l'enfocament de remostreig despre's de particionar. Aixo¿ revela el potencial d'un sistema de prediccio' de part prematur basat en EHG, permetent estrate¿gies orientades al pacient per a millorar la prevencio' del part prematur, el benestar matern-fetal i la gestio' o¿ptima dels recursos hospitalaris. En general, aquesta tesi doctoral proporciona als cl¿'nics eines valuoses per a la presa de decisions en escenaris de risc matern-fetal de part prematur. Permet als cl¿'nics dissenyar estrate¿gies orientades al pacient per a millorar la prevencio' i el maneig del part prematur. La metodologia proposada e's prometedora per al desenvolupament d'un sistema integrat de prediccio' del part prematur que puga millorar la planificacio' de l'embara¿s, optimitzar l'assignacio' de recursos i millorar la qualitat de l'atencio'.[EN] Preterm delivery, defined as birth before 37 weeks of gestation, is a significant global concern with implications for the health of newborns and economic costs. It affects approximately 11% of all births, amounting to more than 15 million individuals worldwide. Current methods for predicting preterm labor lack precision, leading to overdiagnosis and limited practicality in clinical settings. Electrohysterography (EHG) has emerged as a promising alternative by providing relevant information about uterine electrophysiology. However, previous prediction systems based on EHG have not effectively translated into clinical practice, primarily due to biases in handling imbalanced data and the need for robust and generalizable prediction models. This doctoral thesis aims to develop an artificial intelligence based preterm labor prediction system using EHG and obstetric data from women undergoing regular prenatal check-ups. This system entails extracting relevant features, optimizing the feature subspace, and evaluating strategies to address the imbalanced data challenge for robust prediction. The study validates the effectiveness of temporal, spectral, and non-linear features in distinguishing between preterm and term labor cases. Novel entropy measures, namely dispersion and bubble entropy, outperform traditional entropy metrics in identifying preterm labor. Additionally, the study seeks to maximize complementary information while minimizing redundancy and noise features to optimize the feature subspace for accurate preterm delivery prediction by a genetic algorithm. Furthermore, we have confirmed leakage information between train and test data set when generating synthetic samples before data partitioning giving rise to an overestimated generalization capability of the predictor system. These results emphasize the importance of using partitioning-resampling techniques for ensuring data independence between train and test samples. We propose to combine genetic algorithm and resampling method at the same iteration to deal with imbalanced data learning using partition-resampling pipeline, achieving an Area Under the ROC Curve of 94% and Average Precision of 84%. Moreover, the model demonstrates an F1-score and recall of approximately 80%, outperforming existing studies on partition-resampling pipeline. This finding reveals the potential of an EHG-based preterm birth prediction system, enabling patient-oriented strategies for enhanced preterm labor prevention, maternal-fetal well-being, and optimal hospital resource management. Overall, this doctoral thesis provides clinicians with valuable tools for decision-making in preterm labor maternal-fetal risk scenarios. It enables clinicians to design a patient-oriented strategies for enhanced preterm birth prevention and management. The proposed methodology holds promise for the development of an integrated preterm birth prediction system that can enhance pregnancy planning, optimize resource allocation, and ultimately improve the outcomes for both mother and baby.Nieto Del Amor, F. (2023). Design and assessment of a computer-assisted artificial intelligence system for predicting preterm labor in women attending regular check-ups. Emphasis in imbalance data learning technique [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/200900Compendi

A New Strategy to Improve the Performance of PDP-Systems Simulators

One of the major challenges that current P systems simulators have to deal with is to be as efficient as possible. A P system is syntactically described as a membrane structure delimiting regions where multisets of objects evolve by means of evolution rules. According to that, on each computation step, the applicability of the rules for the current P system configuration must be calculated. In this paper we extend previous works that use Rete-based simulation algorithm in order to improve the time consumed during the checking phase in the selection of rules. A new approach is presented, oriented to the acceleration of Population Dynamics P Systems simulations.Ministerio de Economía y Competitividad TIN2012- 3743

A Simulation Workflow for Membrane Computing: From MeCoSim to PMCGPU Through P-Lingua

P system simulators are of high importance in Membrane Computing, since they provide tools to assist on model validation and verification. Keeping a balance between generality and flexibility, on the one side, and efficiency, on the other hand, is always challenging, but it is worth the effort. Besides, in order to prove the feasibility of P system models as practical tools for solving problems and aid in decision making, it is essential to provide functional mechanisms to have all the elements required at disposal of the potential users smoothly integrated in a robust workflow. The aim of this paper is to describe the main components and connections within the approach followed in this pipeline.Ministerio de Industria, Economía y Competitividad TIN2017-89842-

An Improved GPU Simulator For Spiking Neural P Systems

Spiking Neural P (SNP) systems, variants of Psystems (under Membrane and Natural computing), are computing models that acquire abstraction and inspiration from the way neurons 'compute' or process information. Similar to other P system variants, SNP systems are Turing complete models that by nature compute non-deterministically and in a maximally parallel manner. P systems usually trade (often exponential) space for (polynomial to constant) time. Due to this nature, P system variants are currently limited to parallel simulations, and several variants have already been simulated in parallel devices. In this paper we present an improved SNP system simulator based on graphics processing units (GPUs). Among other reasons, current GPUs are architectured for massively parallel computations, thus making GPUs very suitable for SNP system simulation. The computing model, hardware/software considerations, and simulation algorithm are presented, as well as the comparisons of the CPU only and CPU-GPU based simulators.Ministerio de Ciencia e Innovación TIN2009–13192Junta de Andalucía P08-TIC-0420

Platería del siglo XVIII en La Rambla (Córdoba)

En este texto se presentarán una serie de piezas de platería, que forman parte del ajuar de la parroquia de Nuestra Señora de la Asunción de La Rambla (Córdoba). Todas ellas fueron realizadas a lo largo del siglo XVIII dentro de los estilos barroco y rococó. Algunas de ellas proceden de la mano de plateros destacados del gremio cordobés, como fueron Antonio Ruiz “el viejo”, Antonio Ruiz de León “el joven” o Juan Sánchez Soto, entre otros. De otras se estudiará su posible proveniencia de Iberoamérica y se completará con un pequeño grupo de enseres, cuya autoría aún está por determinar, pero que vendrán a completar el panorama setecentista rambleñoIn this text will be presented a series of pieces of silver, which are part of the trousseau of the parish of Our Lady of the Assumption of La Rambla (Cordoba). All of them were made throughout the eighteenth century in the baroque and rococo styles. Some of them come from the hand of prominent silversmiths from the Cordoban guild, such as Antonio Ruiz "el viejo", Antonio Ruiz de León "el joven" or Juan Sánchez Soto, among others. Others will study the possible origin of Ibero-America and will be completed with a small group of items, whose authorship is yet to be determined, but which will come to complete the eighteenth century rambl

Movies Tags Extraction Using Deep Learning

Retrieving information from movies is becoming increasingly demanding due to the enormous amount of multimedia data generated each day. Not only it helps in efficient search, archiving and classification of movies, but is also instrumental in content censorship and recommendation systems. Extracting key information from a movie and summarizing it in a few tags which best describe the movie presents a dedicated challenge and requires an intelligent approach to automatically analyze the movie. In this paper, we formulate movies tags extraction problem as a machine learning classification problem and train a Convolution Neural Network (CNN) on a carefully constructed tag vocabulary. Our proposed technique first extracts key frames from a movie and applies the trained classifier on the key frames. The predictions from the classifier are assigned scores and are filtered based on their relative strengths to generate a compact set of most relevant key tags. We performed a rigorous subjective evaluation of our proposed technique for a wide variety of movies with different experiments. The evaluation results presented in this paper demonstrate that our proposed approach can efficiently extract the key tags of a movie with a good accuracy

How to Disguise Fairy Tales in 21st Century Ireland. A Feminist Analysis of Marian Keyes’ and Cathy Kelly’s Blockbusters

Ireland has suffered many extraordinary changes during the last decades that have made the Emerald Isle a geographical point upon which all eyes are fixed. Despite this metamorphosis, the question is if its population and cultural heritage have been able to cope with the times. Known as a traditionally catholic and conservative country, many social aspects remain unchangeable and those that have evolved may still keep an inner glimpse of the old times that is not always easily recognizable. Undoubtedly, women and all subjects related to them have experienced a revolution. However, data show that true equality is still far from being reached. In this context, literature must be taken as a powerful cultural force that helps create stereotypes and a popular conscience. Thus, this article analyses the success of what has been called “women’s literature”, especially Marian Keyes’ and Cathy Kelly’s bestselling books. It also tries to examine to what extent the traditional ideologies of womanhood are present and by which means their female protagonists attach to the old stereotypes under a mask of modernity and economic boom. Finally, their effects on the female Irish population will also be studied in order to demonstrate that globalization and modern capitalism prove to be unable to change the old myths that lie beneath and keep women in a relegated position