Search

We propose to embed time series in a latent space where pairwise Euclidean distances (EDs) between samples are equal to pairwise dissimilarities in the original space, for a given dissimilarity measure. To this end, we use auto-encoder (AE) and encoder-only neural networks to learn elastic dissimilarity measures, e.g., dynamic time warping (DTW), that are central to time series classification (Bagnall et al., 2017). The learned representations are used in the context of one-class classification (Mauceri et al., 2020) on the datasets of UCR/UEA archive (Dau et al., 2019). Using a 1-nearest neighbor (1NN) classifier, we show that learned representations allow classification performance that is close to that of raw data, but in a space of substantially lower dimensionality. This implies substantial and compelling savings in terms of computational and storage requirements for nearest neighbor time series classification.

Incomplete spinal cord injury (iSCI) causes impairment of reactive balance control, leading to higher fall risk. In our previous work, we found that individuals with iSCI were more likely to exhibit multiple-step response during the lean-and-release (LR) test, where the participant leaned forward while a tether supported 8-12% of the body weight and received a sudden release, inducing reactive steps. Here we investigated the foot placement of people with iSCI during the LR test using margin-of-stability (MOS). Twenty-one individuals with iSCI (age: 56.1 ± 16.1 years old; mass: 72.5 ± 19.0 kg; height: 166 ± 12 cm), and fifteen age- and sex-matched able-bodied (AB) individuals (age: 56.1 ± 12.9 years old; mass: 57.4 ± 10.9 kg; height: 164 ± 8 cm) participated in the study. The participants performed ten trials of the LR test and also completed clinical assessment of balance and strengths, including the Mini-Balance Evaluations Systems Test, the Community Balance and Mobility Scale, gait speed, and lower extremity manual muscle testing. MOS was significantly smaller during multiple-step responses than during single-step responses for both individuals with iSCI and AB counterparts. Using binary logistic regression and receiver operating characteristic analyses, we demonstrated that MOS can distinguish single- and multiple-step responses. In addition, individuals with iSCI demonstrated significantly larger intra-subject variability of MOS compared to AB individuals at first foot contact. Further, we found that MOS correlated with clinical measures of balance including one for reactive balance. We conclude that individuals with iSCI were less likely to demonstrate foot placement with sufficiently large MOS, which may increase the tendency to exhibit multiple-step responses., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Ocean worlds such as Europa and Enceladus are high priority targets in the search for past or extant life beyond Earth. Evidence of life may be preserved in samples of surface ice by processes such as deposition from active plumes, hydrofracturing, or thermal convection. Terrestrial life produces unique distributions of organic molecules that translate into recognizable biosignatures. Identification and quantification of these organic compounds can be achieved by separation science such as capillary electrophoresis coupled to mass spectrometry (CE-MS). However, the data generated by such an instrument can be multiple orders of magnitude larger than what can be transmitted back to Earth during an ocean world's mission. This requires onboard science data analysis capabilities that summarize and prioritize CE-MS observations with limited computational resources. In response, the autonomous capillary electrophoresis mass-spectra examination (ACME) onboard science autonomy system was created for application to the ocean world's life surveyor (OWLS) instrument suite. ACME is able to compress raw mass spectra by two to three orders of magnitude while preserving most of its scientifically relevant information content. This summarization is achieved by the extraction of raw data surrounding autonomously identified ion peaks and the detection and parameterization of unique background regions. Prioritization of the summarized observations is then enabled by providing estimates of scientific utility, including presence of key target compounds, and the uniqueness of an observation relative to previous observations., (© 2022 Jet Propulsion Laboratory, California Institute of Technology. Government sponsorship acknowledged.)