Interpretable Time Series Models for Wastewater Modeling in Combined Sewer Overflows

Climate change poses increasingly complex challenges to our society. Extreme weather events such as floods, wild fires or droughts are becoming more frequent, spontaneous and difficult to foresee or counteract. In this work we specifically address the problem of sewage water polluting surface water bodies after spilling over from rain tanks as a consequence of heavy rain events. We investigate to what extent state-of-the-art interpretable time series models can help predict such critical water level points, so that the excess can promptly be redistributed across the sewage network. Our results indicate that modern time series models can contribute to better waste water management and prevention of environmental pollution from sewer systems. All the code and experiments can be found in our repository: https://github.com/TeodorChiaburu/RIWWER_TimeSeries.Comment: 8 pages, 5 figures, 2 tables, presented at iSCSi 2023 Lisbo

Automated Computational Energy Minimization of ML Algorithms using Constrained Bayesian Optimization

Bayesian optimization (BO) is an efficient framework for optimization of black-box objectives when function evaluations are costly and gradient information is not easily accessible. BO has been successfully applied to automate the task of hyperparameter optimization (HPO) in machine learning (ML) models with the primary objective of optimizing predictive performance on held-out data. In recent years, however, with ever-growing model sizes, the energy cost associated with model training has become an important factor for ML applications. Here we evaluate Constrained Bayesian Optimization (CBO) with the primary objective of minimizing energy consumption and subject to the constraint that the generalization performance is above some threshold. We evaluate our approach on regression and classification tasks and demonstrate that CBO achieves lower energy consumption without compromising the predictive performance of ML models.13 page

Hemodynamic correlates of spontaneous neural activity measured by human whole-head resting state EEG + fNIRS

The brains of awake, resting human subjects display spontaneously occurring neural activity patterns whose magnitude is typically many times greater than those triggered by cognitive or perceptual performance. Such resting state (RS) activity is thought to reflect the functional organization of the brain. In addition, both evoked and RS activation affect local cerebral hemodynamic properties through processes collectively referred to as neurovascular coupling. This is a major topic of interest due to its relationship with pathological conditions that include hypertension, stroke, subarachnoid hemorrhage, and traumatic brain injury. Its investigation calls for an ability to track both the neural and vascular aspects of brain function. We used scalp electroenc ephalography (EEG) which provided a measure of the electrical potentials generated by cortical postsynaptic currents. Simultaneously we utilized functional near-infrared spectroscopy (NIRS) to continuously monitor hemoglobin concentration changes in superficial cortical layers. The multi-modal signal from 18 healthy adult subjects allowed us to investigate the association of neural activity in a range of frequencies over the whole-head to local changes in hemoglobin concentrations. Our results verified the delayed alpha (8-16 Hz) modulation of hemodynamics in posterior areas known from the literature. They also indicated strong beta (16-32 Hz) modulation of hemodynamics. Analysis revealed, however, that beta modulation was likely generated by the alpha-beta coupling in EEG. Signals from the inferior electrode sites were dominated by scalp muscle related activity. Our study aimed to characterize the phenomena related to neurovascular coupling observable by practical, cost-effective, and non-invasive multi-modal techniques

Telomerase activity and telomere length in primary and metastatic tumors from pediatric bone cancer patients

The presence of telomerase activity has been analyzed in almost all tumor types and tumor-derived cell lines. However, there are very few studies that focus on the presence of telomerase activity in bone tumors, and most of them report analysis on very few samples or bone-derived cell lines. The objective of this study was to analyze the telomere length and telomerase activity in primary tumors and metastatic lesions from pediatric osteosarcoma and Ewing's sarcoma patients. The presence of telomerase activity was analyzed by the telomeric repeat amplification protocol assay, and the telomere length was measured by Southern blot. Results were related to survival and clinical outcome. Telomerase activity was detected in 85% of the bone tumor metastases (100% Ewing's sarcomas and 75% osteosarcomas) but only in 12% of the primary tumors (11.1% osteosarcomas and 12.5% Ewing's sarcomas). Bone tumor tissues with telomerase activity had mean telomere lengths 3 kb shorter than those with no detectable telomerase activity (p = 0.041). The presence of telomerase activity was associated with survival (p = 0.009), and longer event-free survival periods were found in patients who lacked telomerase activity compared with those who had detectable telomerase activity levels in their tumor tissues (p = 0.037). The presence of longer telomeres in primary pediatric bone tumors than in metastases could be indicative of alternative mechanisms of lengthening of telomeres for their telomere maintenance rather than telomerase activity. Nevertheless, the activation of telomerase seems to be a crucial step in the malignant progression and acquisition of invasive capability of bone tumors

Measuring mental workload with EEG+fNIRS

We studied the capability of a Hybrid functional neuroimaging technique to quantify human mental workload (MWL). We have used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) as imaging modalities with 17 healthy subjects performing the letter n-back task, a standard experimental paradigm related to working memory (WM). The level of MWL was parametrically changed by variation of n from 0 to 3. Nineteen EEG channels were covering the whole-head and 19 fNIRS channels were located on the forehead to cover the most dominant brain region involved in WM. Grand block averaging of recorded signals revealed specific behaviors of oxygenated-hemoglobin level during changes in the level of MWL. A machine learning approach has been utilized for detection of the level of MWL. We extracted different features from EEG, fNIRS, and EEG+fNIRS signals as the biomarkers of MWL and fed them to a linear support vector machine (SVM) as train and test sets. These features were selected based on their sensitivity to the changes in the level of MWL according to the literature. We introduced a new category of features within fNIRS and EEG+fNIRS systems. In addition, the performance level of each feature category was systematically assessed. We also assessed the effect of number of features and window size in classification performance. SVM classifier used in order to discriminate between different combinations of cognitive states from binary- and multi-class states. In addition to the cross-validated performance level of the classifier other metrics such as sensitivity, specificity, and predictive values were calculated for a comprehensive assessment of the classification system. The Hybrid (EEG+fNIRS) system had an accuracy that was significantly higher than that of either EEG or fNIRS. Our results suggest that EEG+fNIRS features combined with a classifier are capable of robustly discriminating among various levels of MWL. Results suggest that EEG+fNIRS should be preferred to only EEG or fNIRS, in developing passive BCIs and other applications which need to monitor users' MWL

Using Game Engines and Machine Learning to Create Synthetic Satellite Imagery for a Tabletop Verification Exercise

Satellite imagery is regarded as a great opportunity for citizen-based monitoring of activities of interest. Relevant imagery may however not be available at sufficiently high resolution, quality, or cadence -- let alone be uniformly accessible to open-source analysts. This limits an assessment of the true long-term potential of citizen-based monitoring of nuclear activities using publicly available satellite imagery. In this article, we demonstrate how modern game engines combined with advanced machine-learning techniques can be used to generate synthetic imagery of sites of interest with the ability to choose relevant parameters upon request; these include time of day, cloud cover, season, or level of activity onsite. At the same time, resolution and off-nadir angle can be adjusted to simulate different characteristics of the satellite. While there are several possible use-cases for synthetic imagery, here we focus on its usefulness to support tabletop exercises in which simple monitoring scenarios can be examined to better understand verification capabilities enabled by new satellite constellations and very short revisit times.Comment: Annual Meeting of the Institute of Nuclear Materials Management (INMM), Vienn