PyDTS: A Python Toolkit for Deep Learning Time Series Modelling

© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Abstract In this article, the topic of time series modelling is discussed. It highlights the criticality of analysing and forecasting time series data across various sectors, identifying five primary application areas: denoising, forecasting, nonlinear transient modelling, anomaly detection, and degradation modelling. It further outlines the mathematical frameworks employed in a time series modelling task, categorizing them into statistical, linear algebra, and machine- or deep-learning-based approaches, with each category serving distinct dimensions and complexities of time series problems. Additionally, the article reviews the extensive literature on time series modelling, covering statistical processes, state space representations, and machine and deep learning applications in various fields. The unique contribution of this work lies in its presentation of a Python-based toolkit for time series modelling (PyDTS) that integrates popular methodologies and offers practical examples and benchmarking across diverse datasets.Peer reviewe

Double Fourier Integral Analysis based Convolutional Neural Network Regression for High-Frequency Energy Disaggregation

© 2021 IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/ 10.1109/TETCI.2021.3086226Non-Intrusive Load Monitoring aims to extract the energy consumption of individual electrical appliances through disaggregation of the total power load measured by a single smart-meter. In this article we introduce Double Fourier Integral Analysis in the Non-Intrusive Load Monitoring task in order to provide more distinct feature descriptions compared to current or voltage spectrograms. Specifically, the high-frequency aggregated current and voltage signals are transformed into two-dimensional unit cells as calculated by Double Fourier Integral Analysis and used as input to a Convolutional Neural Network for regression. The performance of the proposed methodology was evaluated in the publicly available U.K.-DALE dataset. The proposed approach improves the estimation accuracy by 7.2% when compared to the baseline energy disaggregation setup using current and voltage spectrograms.Peer reviewe

Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

© 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care

Single-subject analyses of magnetoencephalographic evoked responses to the acoustic properties of affective non-verbal vocalizations

Magneto-encephalography (MEG) was used to examine the cerebral response to affective non-verbal vocalizations (ANVs) at the single-subject level. Stimuli consisted of nonverbal affect bursts from the Montreal Affective Voices morphed to parametrically vary acoustical structure and perceived emotional properties. Scalp magnetic fields were recorded in three participants while they performed a 3-alternative forced choice emotion categorization task (Anger, Fear, Pleasure). Each participant performed more than 6000 trials to allow single-subject level statistical analyses using a new toolbox which implements the general linear model (GLM) on stimulus-specific responses (LIMO-EEG). For each participant we estimated ‘simple’ models (including just one affective regressor (Arousal or Valence)) as well as ‘combined’ models (including acoustical regressors). Results from the ‘simple’ models revealed in every participant the significant early effects (as early as ~100 ms after onset) of Valence and Arousal already reported at the group-level in previous work. However, the ‘combined’ models showed that few effects of Arousal remained after removing the acoustically-explained variance, whereas significant effects of Valence remained especially at late stages. This study demonstrates (i) that single-subject analyses replicate the results observed at early stages by group-level studies and (ii) the feasibility of GLM-based analysis of MEG data. It also suggests that early modulation of MEG amplitude by affective stimuli partly reflects their acoustical properties

Selected groundwater studies of EU project AquaTerra leading to large-scale basin considerations

Several local groundwater studies within the EU project AquaTerra in the Basins of the Meuse, Elbe point at significant influences of groundwater on surface water, while the Brévilles Catchment shows a distinct problematic of pesticide loading to groundwater. Further modeling studies are currently being developed. In the Danube Basin no specific groundwater studies were carried out in the framework of AquaTerra. However on larger scales geochemical proxies such as strontium isotope ratios can give an insight into groundwater contributions to the river that reflects an integral signal of the environmental status of the Basin. Future local groundwater studies should be further correlated to the environmental status of rivers nearby.AquaTerra EU Project FP6 Project no. 505428 (GOCE

The Montreal Affective Voices: A validated set of nonverbal affect bursts for research on auditory affective processing

The Montreal Affective Voices consist of 90 nonverbal affect bursts corresponding to the emotions of anger, disgust, fear, pain, sadness, surprise, happiness, and pleasure (plus a neutral expression), recorded by 10 different actors (5 of them male and 5 female). Ratings of valence, arousal, and intensity for eight emotions were collected for each vocalization from 30 participants. Analyses revealed high recognition accuracies for most of the emotional categories (mean of 68%). They also revealed significant effects of both the actors and the participants gender: The highest hit rates (75%) were obtained for female participants rating female vocalizations, and the lowest hit rates (60%) for male participants rating male vocalizations. Interestingly, the mixed situations that is, male participants rating female vocalizations or female participants rating male vocalizations yielded similar, intermediate ratings. The Montreal Affective Voices are available for download at vnl.psy.gla.ac.uk (Resources section)

Abstract Number ‐ 7: Final Angiographic, Clinical and Thrombus Composition Results of 1000 Patients in the EXCELLENT Registry

Introduction EXCELLENT (NCT03685578; Cerenovus) is a large, prospective, international, real‐world registry of endovascular clot removal in acute ischemic stroke (AIS) with EmboTrap as the first line mechanical thrombectomy (MT) device, which included collection and analysis of the retrieved thrombus material. Methods Between September 2018 and March 2021, 1000 “all‐comer” patients were enrolled at 34 global sites (27 US, 5 EU, 1 UK, 1 Israel) and treated according to standard of care at each center (with Embrotrap as first line). The study employed blind endpoint evaluation, including a core imaging lab and an independent 90‐day mRS assessment. Retrieved clot was collected per each MT maneuver from 538 subjects across 26 sites and clot analysis was performed by independent central labs blinded to clinical data. Results mITT population included 998 subjects. Mean age was 69.9±14.18 years (range 18–102), 51.8% (517/998) subjects were female and 9.9% (97/997) had a pre‐stroke mRS 3–5. Baseline NIHSS was 15.6±6.87 (range 0–36); 10.1% (82/815) subjects had a large core (ASPECTS 0–5); 5.8% (57/990) had posterior stroke; 56.3% (523/929) underwent MT ≤ 6hrs of onset and 38.1% (380/998) received IV‐tPA prior to MT. First pass eTICI 2c‐3 was achieved in 38.3% (377/984) and final 2b‐3 in 94.5 % (930/984; median number of passes = 1) of subjects. 90‐day mRS≤2 or ≤pre‐stroke was 46.9% (429/915) and 90‐day all‐cause mortality was 19.0% (175/921). The univariate analysis of clot components showed high red blood cell and low platelet content were significant predictors of good mRS outcome (p < 0.001 and 0.009) and negative predictors of 90‐day mortality (p < 0.001 and 0.017, respectively). Conclusions This large multi‐center, all‐comer cohort reflects the population undergoing thrombectomy today in a real world‐setting. Final angiographic, clinical and thrombus composition results, along with multivariate analysis of predictors of clinical outcomes, will be presented at the time of the conference

Prospective study on embolization of intracranial aneurysms with the pipeline device: the PREMIER study 1 year results

BACKGROUND: Preliminary clinical studies on the safety and efficacy of the pipeline embolization device (PED) for the treatment of small/medium aneurysms have demonstrated high occlusion rates with low complications. OBJECTIVE: To evaluate the safety and effectiveness of the PED for treatment of wide necked small and medium intracranial aneurysms. METHODS: PREMIER is a prospective, multicenter, single arm trial. Patients were treated with the PED for unruptured wide necked aneurysms, measuring ≤12 mm along the internal carotid artery or vertebral artery, between July 2014 and November 2015. At 1 year post-procedure, the primary effectiveness endpoint was complete occlusion (Raymond grade 1) without major parent vessel stenosis (≤50%) or retreatment, and the primary safety endpoint was major stroke in the territory supplied by the treated artery or neurologic death. RESULTS: A total of 141 patients were treated with PEDs (mean age 54.6±11.3 years, 87.9% (124/141) women). Mean aneurysm size was 5.0±1.92 mm, and 84.4% (119/141) measured99.3% (140/141) of patients. Mean number of PEDs implanted per patient was 1.1±0.26; a single PED was used in 92.9% (131/141) of patients. At 1 year, 97.9% (138/141) of patients underwent follow-up angiography with 76.8% (106/138) of patients having met the study\u27s primary effectiveness endpoint. The combined major morbidity and mortality rate was 2.1% (3/140). CONCLUSIONS: Treatment of wide necked small/medium aneurysms with the PED results in high rates of complete occlusion without significant parent vessel stenosis and low rates of permanent neurologic complications. TRIAL REGISTRATION: NCT02186561

Prospective study on embolization of intracranial aneurysms with the pipeline device (PREMIER study): 3-year results with the application of a flow diverter specific occlusion classification

Background: The pipeline embolization device (PED; Medtronic) has presented as a safe and efficacious treatment for small- and medium-sized intracranial aneurysms. Independently adjudicated long-term results of the device in treating these lesions are still indeterminate. We present 3-year results, with additional application of a flow diverter specific occlusion scale. Methods: PREMIER (prospective study on embolization of intracranial aneurysms with pipeline embolization device) is a prospective, single-arm trial. Inclusion criteria were patients with unruptured wide-necked intracranial aneurysms ≤12 mm. Primary effectiveness (complete aneurysm occlusion) and safety (major neurologic event) endpoints were independently monitored and adjudicated. Results: As per the protocol, of 141 patients treated with a PED, 25 (17.7%) required angiographic follow-up after the first year due to incomplete aneurysm occlusion. According to the Core Radiology Laboratory review, three (12%) of these patients progressed to complete occlusion, with an overall rate of complete aneurysm occlusion at 3 years of 83.3% (115/138). Further angiographic evaluation using the modified Cekirge-Saatci classification demonstrated that complete occlusion, neck residual, or aneurysm size reduction occurred in 97.1%. The overall combined safety endpoint at 3 years was 2.8% (4/141), with only one non-debilitating major event occurring after the first year. There was one case of aneurysm recurrence but no cases of delayed rupture in this series. Conclusions: The PED device presents as a safe and effective modality in treating small- and medium-sized intracranial aneurysms. The application of a flow diverter specific occlusion classification attested the long-term durability with higher rate of successful aneurysm occlusion and no documented aneurysm rupture