Prediction of Muscle Performance During Dynamic Repetitive Exercise

A method for predicting human muscle performance was developed. Eight test subjects performed a repetitive dynamic exercise to failure using a Lordex spinal machine. Electromyography (EMG) data was collected from the erector spinae. Evaluation of the EMG data using a 5th order Autoregressive (AR) model and statistical regression analysis revealed that an AR parameter, the mean average magnitude of AR poles, can predict performance to failure as early as the second repetition of the exercise. Potential applications to the space program include evaluating on-orbit countermeasure effectiveness, maximizing post-flight recovery, and future real-time monitoring capability during Extravehicular Activity

Isolation of Precursor Cells from Waste Solid Fat Tissue

A process for isolating tissue-specific progenitor cells exploits solid fat tissue obtained as waste from such elective surgical procedures as abdominoplasties (tummy tucks) and breast reductions. Until now, a painful and risky process of aspiration of bone marrow has been used to obtain a limited number of tissue- specific progenitor cells. The present process yields more tissue-specific progenitor cells and involves much less pain and risk for the patient. This process includes separation of fat from skin, mincing of the fat into small pieces, and forcing a fat saline mixture through a sieve. The mixture is then digested with collagenase type I in an incubator. After centrifugation tissue-specific progenitor cells are recovered and placed in a tissue-culture medium in flasks or Petri dishes. The tissue-specific progenitor cells can be used for such purposes as (1) generating three-dimensional tissue equivalent models for studying bone loss and muscle atrophy (among other deficiencies) and, ultimately, (2) generating replacements for tissues lost by the fat donor because of injury or disease

Homogeneous Vector Capsules Enable Adaptive Gradient Descent in Convolutional Neural Networks

Capsules are the name given by Geoffrey Hinton to vector-valued neurons. Neural networks traditionally produce a scalar value for an activated neuron. Capsules, on the other hand, produce a vector of values, which Hinton argues correspond to a single, composite feature wherein the values of the components of the vectors indicate properties of the feature such as transformation or contrast. We present a new way of parameterizing and training capsules that we refer to as homogeneous vector capsules (HVCs). We demonstrate, experimentally, that altering a convolutional neural network (CNN) to use HVCs can achieve superior classification accuracy without increasing the number of parameters or operations in its architecture as compared to a CNN using a single final fully connected layer. Additionally, the introduction of HVCs enables the use of adaptive gradient descent, reducing the dependence a model's achievable accuracy has on the finely tuned hyperparameters of a non-adaptive optimizer. We demonstrate our method and results using two neural network architectures. First, a very simple monolithic CNN that when using HVCs achieved a 63% improvement in top-1 classification accuracy and a 35% improvement in top-5 classification accuracy over the baseline architecture. Second, with the CNN architecture referred to as Inception v3 that achieved similar accuracies both with and without HVCs. Additionally, the simple monolithic CNN when using HVCs showed no overfitting after more than 300 epochs whereas the baseline showed overfitting after 30 epochs. We use the ImageNet ILSVRC 2012 classification challenge dataset with both networks.https://arxiv.org/abs/1906.08676v

Homogeneous Vector Capsules Enable Adaptive Gradient Descent in Convolutional Neural Networks

Copyright © 2021 The Author(s). Neural networks traditionally produce a scalar value for an activated neuron. Capsules, on the other hand, produce a vector of values, which has been shown to correspond to a single, composite feature wherein the values of the components of the vectors indicate properties of the feature such as transformation or contrast. We present a new way of parameterizing and training capsules that we refer to as homogeneous vector capsules (HVCs). We demonstrate, experimentally, that altering a convolutional neural network (CNN) to use HVCs can achieve superior classification accuracy without increasing the number of parameters or operations in its architecture as compared to a CNN using a single final fully connected layer. Additionally, the introduction of HVCs enables the use of adaptive gradient descent, reducing the dependence a model’s achievable accuracy has on the finely tuned hyperparameters of a non-adaptive optimizer. We demonstrate our method and results using two neural network architectures. For the CNN architecture referred to as Inception v3, replacing the fully connected layers with HVCs increased the test accuracy by an average of 1.32% across all experiments conducted. For a simple monolithic CNN, we show HVCs improve test accuracy by an average of 19.16%

Towards an Analytical Definition of Sufficient Data

Copyright © 2022 The Author(s). We show that, for each of five datasets of increasing complexity, certain training samples are more informative of class membership than others. These samples can be identified a priori to training by analyzing their position in reduced dimensional space relative to the classes' centroids. Specifically, we demonstrate that samples nearer the classes' centroids are less informative than those that are furthest from it. For all five datasets, we show that there is no statistically significant difference between training on the entire training set and when excluding up to 2% of the data nearest to each class's centroid

Class Density and Dataset Quality in High-Dimensional, Unstructured Data

Copyright © 2022 The Authors. We provide a definition for class density that can be used to measure the aggregate similarity of the samples within each of the classes in a high-dimensional, unstructured dataset. We then put forth several candidate methods for calculating class density and analyze the correlation between the values each method produces with the corresponding individual class test accuracies achieved on a trained model. Additionally, we propose a definition for dataset quality for high-dimensional, unstructured data and show that those datasets that met a certain quality threshold (experimentally demonstrated to be > 10 for the datasets studied) were candidates for eliding redundant data based on the individual class densities

Microwave/Sonic Apparatus Measures Flow and Density in Pipe

An apparatus for measuring the rate of flow and the mass density of a liquid or slurry includes a special section of pipe instrumented with microwave and sonic sensors, and a computer that processes digitized readings taken by the sensors. The apparatus was conceived specifically for monitoring a flow of oil-well-drilling mud, but the basic principles of its design and operation are also applicable to monitoring flows of other liquids and slurries

no Routing Needed Between Capsules

Copyright © 2021 The Authors. Most capsule network designs rely on traditional matrix multiplication between capsule layers and computationally expensive routing mechanisms to deal with the capsule dimensional entanglement that the matrix multiplication introduces. By using Homogeneous Vector Capsules (HVCs), which use element-wise multiplication rather than matrix multiplication, the dimensions of the capsules remain unentangled. In this work, we study HVCs as applied to the highly structured MNIST dataset in order to produce a direct comparison to the capsule research direction of Geoffrey Hinton, et al. In our study, we show that a simple convolutional neural network using HVCs performs as well as the prior best performing capsule network on MNIST using 5.5× fewer parameters, 4× fewer training epochs, no reconstruction sub-network, and requiring no routing mechanism. The addition of multiple classification branches to the network establishes a new state of the art for the MNIST dataset with an accuracy of 99.87% for an ensemble of these models, as well as establishing a new state of the art for a single model (99.83% accurate)

On the Importance of Capturing a Sufficient Diversity of Perspective for the Classification of micro-PCBs

Pre-print of an original work presented at KES-IDT 2021 held virtually.We present a dataset consisting of high-resolution images of 13 micro-PCBs captured in various rotations and perspectives relative to the camera, with each sample labeled for PCB type, rotation category, and perspective categories. We then present the design and results of experimentation on combinations of rotations and perspectives used during training and the resulting impact on test accuracy. We then show when and how well data augmentation techniques are capable of simulating rotations vs. perspectives not present in the training data. We perform all experiments using CNNs with and without homogeneous vector capsules (HVCs) and investigate and show the capsules' ability to better encode the equivariance of the sub-components of the micro-PCBs. The results of our experiments lead us to conclude that training a neural network equipped with HVCs, capable of modeling equivariance among sub-components, coupled with training on a diversity of perspectives, achieves the greatest classification accuracy on micro-PCB data

Medium-Frequency Pseudonoise Georadar

Ground-probing radar systems featuring medium-frequency carrier signals phase-modulated by binary pseudonoise codes have been proposed. These systems would be used to locate and detect movements of subterranean surfaces; the primary intended application is in warning of the movement of underground water toward oil-well intake ports in time to shut down those ports to avoid pumping of water. Other potential applications include oil-well logging and monitoring of underground reservoirs. A typical prior georadar system operates at a carrier frequency of at least 50 MHz in order to provide useable range resolution. This frequency is too high for adequate penetration of many underground layers of interest. On the other hand, if the carrier frequency were to be reduced greatly to increase penetration, then bandwidth and thus range resolution would also have to be reduced, thereby rendering the system less useful. The proposed medium-frequency pseudonoise georadar systems would offer the advantage of greater penetration at lower carrier frequencies, but without the loss of resolution that would be incurred by operating typical prior georadar systems at lower frequencies