Code Detection for Hardware Acceleration Using Large Language Models

Large language models (LLMs) have been massively applied to many tasks, often surpassing state-of-the-art approaches. While their effectiveness in code generation has been extensively studied (e.g., AlphaCode), their potential for code detection remains unexplored. This work presents the first analysis of code detection using LLMs. Our study examines essential kernels, including matrix multiplication, convolution, and fast-fourier transform, implemented in C/C++. We propose both a preliminary, naive prompt and a novel prompting strategy for code detection. Results reveal that conventional prompting achieves great precision but poor accuracy (68.8%, 22.3%, and 79.2% for GEMM, convolution, and FFT, respectively) due to a high number of false positives. Our novel prompting strategy substantially reduces false positives, resulting in excellent overall accuracy (91.1%, 97.9%, and 99.7%, respectively). These results pose a considerable challenge to existing state-of-the-art code detection methods

Expanding the deep-learning model to diagnosis LVNC: limitations and trade-offs

©2024. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Published Manuscript version of a Published Work that appeared in final form in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization. To access the final edited and published work see https://doi.org/10.1080/21681163.2024.2314566Hyper-trabeculation or non-compaction in the left ventricle of the myocardium (LVNC) is a recently classified form of cardiomyopathy. Several methods have been proposed to quantify the trabeculae accurately in the left ventricle, but there is no general agreement in the medical community to use a particular approach. In the previous work, we proposed DL-LVTQ, a deep-learning approach for left ventricular trabecular quantification based on a U-Net CNN architecture. In this work, we have extended and adapted DL-LVTQ to cope with patients with different particularities and cardiomyopathies. Patient images were taken from different scanners and hospitals. We have modified and adapted the U-Net convolutional neural network to account for the different particularities of a heterogeneous group of patients with multiple cardiomyopathies and inherited cardiomyopathies. The inclusion of new groups of patients has increased the accuracy, specificity and Kappa values while maintaining the sensitivity of the proposed method. Therefore, a better-prepared diagnosis tool is ready for various cardiomyopathies with different characteristics. Cardiologists have considered that 98.9% of the evaluated outputs are verified clinically for diagnosis. Therefore, the high precision to segment the different cardiac structures allows us to make a robust diagnostic system bjective and faster, decreasing human error and time spent

The Use of Amniotic Membrane in the Management of Complex Chronic Wounds

Chronic wounds do not follow the usual wound healing process; instead, they are stuck in the inflammatory or proliferative phase. This is particularly evident in large, massive wounds with considerable tissue loss, which become senescent and do not epithelialize. In these wounds, we need to remove all the factors that prevent or delay normal wound healing. After that, soft tissue granulation is stimulated by local negative pressure therapy. Lastly, after the granulation is completed, the epithelialization process must be activated. Although a plethora of wound dressings and devices are available, chronic wounds persist as a unresolved medical concern. We have been using frozen amniotic membrane (AM) to treat this type of wounds with good results. Our studies have shown that AM is able to induce epithelialization in large wounds that were unable to epithelialize. AM induces several signaling pathways involved in cell migration and/or proliferation. Among those, we can highlight the mitogen‐activated protein kinase (MAPK) and Jun N‐terminal kinase (JNK) signaling pathways. Additionally, AM is able to selectively antagonise the anti-proliferative effect of TGFß by modifying its genetic program on keratinocytes. The combined effect of AM on keratinocytes, promoting cell proliferation/migration and antagonising TGFß-effect, is the perfect combination allowing chronic wounds to progress into epithelialization

Exploiting hybrid parallelism in the kinematic analysis of multibody systems based on group equations

Computational kinematics is a fundamental tool for the design, simulation, control, optimization and dynamic analysis of multibody systems. The analysis of complex multibody systems and the need for real time solutions requires the development of kinematic and dynamic formulations that reduces computational cost, the selection and efficient use of the most appropriated solvers and the exploiting of all the computer resources using parallel computing techniques. The topological approach based on group equations and natural coordinates reduces the computation time in comparison with well-known global formulations and enables the use of parallelism techniques which can be applied at different levels: simultaneous solution of equations, use of multithreading routines, or a combination of both. This paper studies and compares these topological formulation and parallel techniques to ascertain which combination performs better in two applications. The first application uses dedicated systems for the real time control of small multibody systems, defined by a few number of equations and small linear systems, so shared-memory parallelism in combination with linear algebra routines is analyzed in a small multicore and in Raspberry Pi. The control of a Stewart platform is used as a case study. The second application studies large multibody systems in which the kinematic analysis must be performed several times during the design of multibody systems. A simulator which allows us to control the formulation, the solver, the parallel techniques and size of the problem has been developed and tested in more powerful computational systems with larger multicores and GPU.This work was supported by the Spanish MINECO, as well as European Commission FEDER funds, under grant TIN2015-66972-C5-3-

Improving a Deep Learning Model to Accurately Diagnose LVNC

©2023. This manuscript version is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Published Manuscript version of a Published Work that appeared in final form in Journal of Clinical Medicine. To access the final edited and published work see https://doi.org/10.3390/jcm12247633Accurate diagnosis of Left Ventricular Noncompaction Cardiomyopathy (LVNC) is critical for proper patient treatment but remains challenging. This work improves LVNC detection by improving left ventricle segmentation in cardiac MR images. Trabeculated left ventricle indicates LVNC, but automatic segmentation is difficult. We present techniques to improve segmentation and evaluate their impact on LVNC diagnosis. Three main methods are introduced: (1) using full 800 × 800 MR images rather than 512 × 512; (2) a clustering algorithm to eliminate neural network hallucinations; (3) advanced network architectures including Attention U-Net, MSA-UNet, and U-Net++.Experiments utilize cardiac MR datasets from three different hospitals. U-Net++ achieves the best segmentation performance using 800 × 800 images, and it improves the mean segmentation Dice score by 0.02 over the baseline U-Net, the clustering algorithm improves the mean Dice score by 0.06 on the images it affected, and the U-Net++ provides an additional 0.02 mean Dice score over the baseline U-Net. For LVNC diagnosis, U-Net++ achieves 0.896 accuracy, 0.907 precision, and 0.912 F1-score outperforming the baseline U-Net. Proposed techniques enhance LVNC detection, but differences between hospitals reveal problems in improving generalization. This work provides validated methods for precise LVNC diagnosis

Trabeculated Myocardium in Hypertrophic Cardiomyopathy: Clinical Consequences

Aims: Hypertrophic cardiomyopathy (HCM) is often accompanied by increased trabeculated myocardium (TM)-which clinical relevance is unknown. We aim to measure the left ventricular (LV) mass and proportion of trabeculation in an HCM population and to analyze its clinical implication. Methods and Results: We evaluated 211 patients with HCM (mean age 47.8 +/- 16.3 years, 73.0% males) with cardiac magnetic resonance (CMR) studies. LV trabecular and compacted mass were measured using dedicated software for automatic delineation of borders. Mean compacted myocardium (CM) was 160.0 +/- 62.0 g and trabecular myocardium (TM) 55.5 +/- 18.7 g. The percentage of trabeculated myocardium (TM%) was 26.7% +/- 6.4%. Females had significantly increased TM% compared to males (29.7 +/- 7.2 vs. 25.6 +/- 5.8, p < 0.0001). Patients with LVEF < 50% had significantly higher values of TM% (30.2% +/- 6.0% vs. 26.6% +/- 6.4%, p = 0.02). Multivariable analysis showed that female gender and neutral pattern of hypertrophy were directly associated with TM%, while dynamic obstruction, maximal wall thickness and LVEF% were inversely associated with TM%. There was no association between TM% with arterial hypertension, physical activity, or symptoms. Atrial fibrillation and severity of hypertrophy were the only variables associated with cardiovascular death. Multivariable analysis failed to demonstrate any correlation between TM% and arrhythmias. Conclusions: Approximately 25% of myocardium appears non-compacted and can automatically be measured in HCM series. Proportion of non-compacted myocardium is increased in female, non-obstructives, and in those with lower contractility. The amount of trabeculation might help to identify HCM patients prone to systolic heart failure

El método docente y resultados de la asignatura Ampliación y Estructura de Computadores

En este trabajo se describe la asignatura de Ampliación y Estructura de Computadores que se imparte en el Grado de Ingeniería Informática de la Universidad de Murcia.  Se trata de una asignatura de seis créditos ECTS en los que el alumno dispone de 150 horas para asistir a las clases teóricas y a las sesiones de prácticas, realizar su trabajo autónomo y llevar a cabo la evaluación correspondiente.  En el artículo se analiza la metodología de enseñanza, la temporización, la coordinación, la evaluación de la asignatura, los resultados obtenidos por los alumnos durante distintos cursos académicos y la satisfacción de los alumnos con la actividad docente.This paper describes the subject on Advanced Computer Structure given in the Computer Engineering Degree at the University of Murcia. The subject carries six ECTS credits and students have 150 hours during which they attend theory classes and practicals, and carry out their personal work along with the corresponding evaluation. This article analyzes the teaching methodology, the timing, the coordination, the assessment of the subject, the results obtained by students over various academic years and the level of satisfaction by students of the teaching

Thesis overview: Design, evaluation and optimization of the wavelet transform for medical video coding in single processor architectures

In this work, we present a new lossy video compression scheme, based on the use of the 3D Fast Wavelet Transform (3D-FWT). This encoder achieves high compression ratio and a quality excellent from the point of medical view, because there are no differences between the original and the reconstructed video. On the other hand, we propose several techniques to allow the real-time video compression and transmission of the 3D-FWT in single processor architectures. We mitigate the memory problem by exploiting the memory hierarchy of the processor using the blocking techniques. We also put forward the reuse of previous computations in order to decrease the number of memory accesses and floating point operations. (Párrafo extraído del texto a modo de resumen)Facultad de Informátic

Diseño, evaluación y optimización de la transformación Wavelet para codificación de vídeo médico en arquitecturas monoprocesador / Gregorio Bernabé García ; director José Manuel García Carrasco, José González González.

Tesis-Universidad de Murcia.Consulte la tesis en: BCA. GENERAL. ARCHIVO UNIVERSITARIO. T.M. 2766