A Time-aware tensor decomposition for tracking evolving patterns

Time-evolving data sets can often be arranged as a higher-order tensor with one of the modes being the time mode. While tensor factorizations have been successfully used to capture the underlying patterns in such higher-order data sets, the temporal aspect is often ignored, allowing for the reordering of time points. In recent studies, temporal regularizers are incorporated in the time mode to tackle this issue. Nevertheless, existing approaches still do not allow underlying patterns to change in time (e.g., spatial changes in the brain, contextual changes in topics). In this paper, we propose temporal PARAFAC2 (tPARAFAC2): a PARAFAC2-based tensor factorization method with temporal regularization to extract gradually evolving patterns from temporal data. Through extensive experiments on synthetic data, we demonstrate that tPARAFAC2 can capture the underlying evolving patterns accurately performing better than PARAFAC2 and coupled matrix factorization with temporal smoothness regularization.Comment: 6 pages, 5 figure

Εκφράζοντας τις πολυγραμμικές μεθόδους PCA και LDA ως προβλήματα ελαχίστων τετραγώνων.

Αν και η πρώτη ερευνητική δραστηριότητα σχετικά με την Ανάλυση Συνιστωσών (Component Analysis - CA) εμφανίστηκε αρκετές δεκαετίες πριν, ο τομέας αυτός είναι ακόμη αρκετά ενεργός. Δοσμένου ενός συνόλου δεδομένων, μία μέθοδος CA υπολογίζει μια απεικόνιση (mapping) των αρχικών δεδομένων, στην οποία τα χαρακτηριστικά κάθε δείγματος θα εξυπηρετούν καλύτερα τα διαθέσιμα εργαλεία και τον εκάστοτε σκοπό. Συνήθως, η προκύπτουσα προβολή έχει λιγότερα χαρακτηριστικά από το σύνολο εισόδου και συνεπώς η προσέγγιση αυτή ειναι γνωστή και ως Μείωση Διαστάσεων (Dimensionality Reduction). Παρόλο που αυτοί οι μέθοδοι ήταν αρχικά σχεδιασμένοι για διανυσματικά δεδομένα, η ανάγκη για ανάλυση πολυδιάστατων δεδομένων αποτέλεσε όχημα για την επέκταση τους σε τανυστές. Σε αυτήν την διπλωματική εργασία, θα εστιάσουμε σε δύο τέτοιες επεκτάσεις: την Πολυγραμμική Ανάλυση Κύριων Συνιστωσών (Multilinear Principal Component Analysis – MPCA) και την Ανάλυση Διάκρισης με Αναπαράσταση Τανυστή (Discriminant Analysis with Tensor Representation – DATER) και θα παρουσιάσουμε πώς διατυπώνονται ως προβλήματα εύρεσης ιδιοτιμών και ιδιοδιανυσμάτων. Μια τέτοια διατύπωση, ωστόσο, εμπεριέχει τα εξής προβλήματα: (1) δεν απαγορεύει την επίλυση προβλημάτων εύρεσης ιδιοτιμών και ιδιοδιανυσμάτων σε πίνακες κακής κατάστασης (ill-conditioned matrices), πράγμα που ισχύει αρκετά συχνά σε δεδομένα τανυστών [1] και (2) οι εμπλεκόμενοι πίνακες έχουν μεγάλες διαστάσεις και η επίλυση τέτοιων προβλημάτων απαιτεί αρκετό χρόνο. Για το σκοπό αυτό, προτείνουμε έναν τρόπο διατύπωσης των MPCA και DATER ως προβλήματα Παλινδρόμησης Τανυστών, έτσι ώστε να μπορούν να εφαρμοστούν περισσότερο αριθμητικά ευσταθείς και υπολογιστικά απλούστερες προσεγγίσεις (π.χ. Gradient Descent). Κατόπιν, εξετάζουμε την ποιότητα της πρότασης μας σε πραγματικά δεδομένα με πείραματα Αφαίρεσης Θορύβου (Image Denoising) και Αναγνώρισης Προσώπου (Face Recognition).Although the first works relevant to Component Analysis (CA) date many decades ago, it still remains a very active research area. Given a dataset, CA methods aim to find a mapping of it, the features of which are ideal for the available tools or the assigned task. Typically, the produced mapping has fewer features than the original data, therefore this approach is also known as Dimensionality Reduction. While these methods were designed to work on vectors, the need to analyze multidimensional datasets with an abundance of features, fueled their extension to tensors. In this thesis, we will investigate two such extensions, Multilinear Principal Component Analysis (MPCA) and Discriminant Analysis with Tensor Representation (DATER) and present how they are formulated as generalized eigenproblems. Such formulation, however, conceals several drawbacks: (1) it may require solving eigenproblems on ill-conditioned matrices, which is more than often the case when it comes to tensor data [1] and (2) the matrices involved are commonly highly dimensional and solving for their eigenvalues requires significant computation time. To this end, we will propose a Least Squares (LS) Tensor Regression formulation for MPCA and DATER, which makes applicable more numerically stable and computationally simpler approaches (e.g., Gradient Descent) and evaluate it in practice with an Image Denoising and Face Recognition task

MORCIC: Model Order Reduction Techniques for Electromagnetic Models of Integrated Circuits

Model order reduction (MOR) is crucial for the design process of integrated circuits. Specifically, the vast amount of passive RLCk elements in electromagnetic models extracted from physical layouts exacerbates the extraction time, the storage requirements, and, most critically, the post-layout simulation time of the analyzed circuits. The MORCIC project aims to overcome this problem by proposing new MOR techniques that perform better than commercial tools. Experimental evaluation on several analog and mixed-signal circuits with millions of elements indicates that the proposed methods lead to x5.5 smaller ROMs while maintaining similar accuracy compared to golden ROMs provided by ANSYS RaptorX.Comment: arXiv admin note: substantial text overlap with arXiv:2311.0847

Predicting lymphoma in Sjögren's syndrome and the pathogenetic role of parotid microenvironment through precise parotid swelling recording

Objective: Parotid swelling (PSW) is a major predictor of non-Hodgkin lymphoma (NHL) in primary Sjögren's syndrome (pSS). However, since detailed information on the time of onset and duration of PSW is scarce, this was investigated to verify whether it may lead to further improved prediction. NHL localisation was concomitantly studied to evaluate the role of the parotid gland microenvironment in pSS-related lymphomagenesis. Methods: A multicentre study was conducted among patients with pSS who developed B cell NHL during follow-up and matched controls that did not develop NHL. The study focused on the history of salivary gland and lachrymal gland swelling, evaluated in detail at different times and for different durations, and on the localisation of NHL at onset. Results: PSW was significantly more frequent among the cases: at the time of first referred pSS symptoms before diagnosis, at diagnosis, and from pSS diagnosis to NHL. The duration of PSW was evaluated starting from pSS diagnosis, and the NHL risk increased from PSW of 2-12 months to > 12 months. NHL was prevalently localised in the parotid glands of the cases. Conclusion: A more precise clinical recording of PSW can improve lymphoma prediction in pSS. PSW as a very early symptom is a predictor, and a longer duration of PSW is associated with a higher risk of NHL. Since lymphoma usually localises in the parotid glands, and not in the other salivary or lachrymal glands, the parotid microenvironment appears to be involved in the whole history of pSS and related lymphomagenesis

An analysis of factors that influence personal exposure to toluene and xylene in residents of Athens, Greece

BACKGROUND: Personal exposure to pollutants is influenced by various outdoor and indoor sources. The aim of this study was to evaluate the exposure of Athens citizens to toluene and xylene, excluding exposure from active smoking. METHODS: Passive air samplers were used to monitor volunteers, their homes and various urban sites for one year, resulting in 2400 measurements of toluene and xylene levels. Since both indoor and outdoor pollution contribute significantly to human exposure, volunteers were chosen from occupational groups who spend a lot of time in the streets (traffic policemen, bus drivers and postmen), and from groups who spend more time indoors (teachers and students). Data on individual and house characteristics were obtained using a questionnaire completed at the beginning of the study; a time-location-activity diary was also completed daily by the volunteers in each of the six monitoring campaigns. RESULTS: Average personal toluene exposure varied over the six monitoring campaigns from 53 to 80 μg/m(3). Urban and indoor concentrations ranged from 47 – 84 μg/m(3 )and 30 – 51 μg/m(3), respectively. Average personal xylene exposure varied between 56 and 85 μg/m(3 )while urban and indoor concentrations ranged from 53 – 88 μg/m(3 )and 27 – 48 μg/m(3), respectively. Urban pollution, indoor residential concentrations and personal exposures exhibited the same pattern of variation during the measurement periods. This variation among monitoring campaigns might largely be explained by differences in climate parameters, namely wind speed, humidity and amount of sunlight. CONCLUSION: In Athens, Greece, the time spent outdoors in the city center during work or leisure makes a major contribution to exposure to toluene and xylene among non-smoking citizens. Indoor pollution and means of transportation contribute significantly to individual exposure levels. Other indoor residential characteristics such as recent painting and mode of heating used might also contribute significantly to individual levels. Groups who may be subject to higher exposures (e.g. those who spent more time outdoors because of occupational activities) need to be surveyed and protected against possible adverse health effects

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Correction to: Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

The original version of this article unfortunately contained a mistake

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Gated Mixture Variational Autoencoders for Value Added Tax audit case selection

In this work, we address the problem of targeted Value Added Tax (VAT) audit case selection by means of machine learning. This is a challenging problem that has remained rather elusive for EU-based Tax Departments, due to the inadequate quantity of tax audits that can be used for conventional supervised model training. To this end, we devise a novel Gated Mixture Variational Autoencoder deep network, that can be effectively trained with data from a limited number of audited taxpayers, combined with a large corpus of filed VAT returns. This gives rise to a semi-supervised learning framework that leverages the latest advances in deep learning and robust regularization using variational inference. We developed our approach in collaboration with the Cyprus Tax Department and experimentally deployed it to facilitate its audit selection process; to this end, we used actual VAT data from Cyprus-based taxpayers. This way, we obtained strong empirical evidence that our approach can greatly facilitate the VAT audit case selection process. Specifically, we obtained up to 76% out-of-sample accuracy in detecting whether a significant tax yield will be generated from a specific prospective VAT audit