medRxivLeveraging diffusion-based synthetic data to improve diagnostic classification of structural heart disease

This study explores diffusion-based generative modeling using a Denoising Diffusion Probabilistic Model (DDPM) framework to conditionally synthesize chest X-rays from echocardiographic measurements and demographic features. We evaluate whether synthetic images produced by the model can enhance the performance of a diagnostic convolutional neural network in detecting structural heart disease across varying combinations of real and synthetic data.

JAMIAThe incremental design of a machine learning framework for medical records processing

This work presents the development and evaluation of coordn8, a web-based application that streamlines fax processing in outpatient clinics using a “human-in-the-loop” machine learning framework. We demonstrate the effectiveness of the platform at reducing fax processing time and producing accurate machine learning inferences across the tasks of patient identification, document classification, spam classification, and duplicate document detection.

AACRIdentifying the transcriptomic signatures of mutational heterogeneity in GBM using single cell genomics

Glioblastoma (GBM), the deadliest primary brain tumor, exhibits high therapeutic resistance and recurrence due to its genomic and cellular diversity. This study utilizes single-cell genomic analyses to correlate somatic mutations with transcriptomic profiles within tumor tissues. We established a concise three-stage pipeline: variant calling, machine learning (ML) classification, and biological pathway analysis.

IEEE Big DataLearning to Simplify Distributed Systems Management

Managing large-scale distributed systems is challenging. System administrators handle the upkeep of complex, microservices-based architectures with potentially thousands of interconnected nodes. They rely on analyzing logs and metrics from various services, but the sheer volume of data introduces complexity and scaling challenges. To address these issues, we introduce Minerva, an unsupervised Machine Learning (ML) framework for network diagnosis analysis.

NetAIDeepConf: Automating data center network topologies management with machine learning

In this vision paper, we argue that many data center networking techniques, e.g., routing, topology augmentation, energy savings, with diverse goals share design and architectural similarities. We present a framework for developing general intermediate representations of network topologies using deep learning that is amenable to solving a large class of data center problems.

MiddlewareDarnet: A deep learning solution for distracted driving detection

Distracted driving is known to be the leading cause of motor vehicle accidents. In this work, we present a unified data collection and analysis framework, DarNet, capable of detecting and classifying distracted driving behavior using a multimodal CNN+RNN framework.

SECeprivateeye: To the edge and beyond!

Edge computing offers resource-constrained devices low-latency access to high-performance computing infrastructure. In this paper, we present ePrivateEye, an implementation of PrivateEye that offloads computationally expensive computer-vision processing to an edge server.