The extent to which advanced waveform analysis of non-invasive physiological signals can diagnose levels of hypovolemia remains insufficiently explored. The present study explores the discriminative ability of a deep learning (DL) framework to classify levels of ongoing hypovolemia, simulated via novel dynamic lower body negative pressure (LBNP) model among healthy volunteers. We used a dynamic LBNP protocol as opposed to the traditional model, where LBNP is applied in a predictable step-wise, progressively descending manner. This dynamic LBNP version assists in circumventing the problem posed in terms of time dependency, as in real-life pre-hospital settings, intravascular blood volume may fluctuate due to volume resuscitation. A supervised DL-based framework for ternary classification was realized by segmenting the underlying noninvasive signal and labeling segments with corresponding LBNP target levels. The proposed DL model with two inputs was trained with respective time-frequency representations extracted on waveform segments to classify each of them into blood volume loss: Class 1 (mild); Class 2 (moderate); or Class 3 (severe). At the outset, the latent space derived at the end of the DL model via late fusion among both inputs assists in enhanced classification performance. When evaluated in a 3-fold cross-validation setup with stratified subjects, the experimental findings demonstrated PPG to be a potential surrogate for variations in blood volume with average classification performance, AUROC: 0.8861, AUPRC: 0.8141, $F1$-score:72.16%, Sensitivity:79.06 %, and Specificity:89.21 %. Our proposed DL algorithm on PPG signal demonstrates the possibility of capturing the complex interplay in physiological responses related to both bleeding and fluid resuscitation using this challenging LBNP setup.

We present a deep-learning artificial intelligence model that is capable of learning and forecasting the late-inspiral, merger and ringdown of numerical relativity waveforms that describe quasi-circular, spinning, non-precessing binary black hole mergers. We used the NRHybSur3dq8 surrogate model to produce train, validation and test sets of $\ell=|m|=2$ waveforms that cover the parameter space of binary black hole mergers with mass-ratios $q\leq8$ and individual spins $|s^z_{\{1,2\}}| \leq 0.8$. These waveforms cover the time range $t\in[-5000\textrm{M}, 130\textrm{M}]$, where $t=0M$ marks the merger event, defined as the maximum value of the waveform amplitude. We harnessed the ThetaGPU supercomputer at the Argonne Leadership Computing Facility to train our AI model using a training set of 1.5 million waveforms. We used 16 NVIDIA DGX A100 nodes, each consisting of 8 NVIDIA A100 Tensor Core GPUs and 2 AMD Rome CPUs, to fully train our model within 3.5 hours. Our findings show that artificial intelligence can accurately forecast the dynamical evolution of numerical relativity waveforms in the time range $t\in[-100\textrm{M}, 130\textrm{M}]$. Sampling a test set of 190,000 waveforms, we find that the average overlap between target and predicted waveforms is $\gtrsim99\%$ over the entire parameter space under consideration. We also combined scientific visualization and accelerated computing to identify what components of our model take in knowledge from the early and late-time waveform evolution to accurately forecast the latter part of numerical relativity waveforms. This work aims to accelerate the creation of scalable, computationally efficient and interpretable artificial intelligence models for gravitational wave astrophysics.

To explore anomaly detection, we'll be using an EKG data set from PhysioNet, which is essentially the squishy version of the data we'll be getting from servers. Since this data has a very regular waveform, it provides a good vehicle for us to explore the algorithms without getting bogged down in the complications that come with real-world data. The data is supplied in the a02.dat file. A Python module ekg_data.py is provided to read the data. Let's get started by importing the EKG data module and examining what the data looks like: