The Dynamic Defense of Network as POMDP and the DESPOT POMDP solver

Όλοι ακούμε για την Τεχνητή Νοημοσύνη που τα τελευταία χρόνια αποτελεί όλο και μεγαλύτερο κομμάτι της ζωής μας με εφαρμογές που οι περισσότεροι δε θα φανταζόμασταν ποτέ. Η αναπαράσταση του πραγματικού κόσμου απαιτεί πολύπλοκα μοντέλα που να μπορούμε να δώσουμε σε πράκτορες και να δούμε πώς θα ενεργήσουν. Οι Μαρκοβιανές Διαδικασίες Αποφάσεων (MDP) και κυρίως οι Μερικώς Παρατηρούμενες Μαρκοβιανές Διαδικασίές Αποφάσεων (POMDP) αφορούν τη λήψη αποφάσεων υπό αβεβαιότητα και βοηθούν ιδιαίτερα στην πιστή αναπαράσταση ενός περιβάλλοντος. Οι δυνατότητες φαίνονται ατελείωτες, καθώς οι εφαρμογές κυμαίνονται από «έξυπνους» παίκτες παιγνίων μέχρι αυτοματοποιημένα συστήματα οδήγησης. Ένα τέτοιο πρόβλημα που κεντρίζει συνεχώς το ενδιαφέρον είναι η αυτοματοποιημένη άμυνα ενός δικτύου, δηλαδή ένα δίκτυο που προστατεύεται μόνο του από επίδοξους εισβολείς, προβλέποντας τις κινήσεις τους και παίρνοντας τα κατάλληλα μέτρα ώστε να τους αποτρέψει από το να φτάσουν σε ζωτικά σημεία του δικτύου. Οι επιτηθέμενοι δεν κάνουν απλές ενέργειες, αλλά χρησιμοποιούν πολύπλοκες τακτικές συνδυάζοντας πολλά τρωτά σημεία του δικτύου κι έτσι η ανάπτυξη ενός τέτοιου συστήματος άμυνας καθίσταται αρκετά δύσκολη. Αν και μπορούμε να αναπαραστίσουμε το πρόβλημα αρκετά πιστά σαν POMDP, υπάρχει το ζήτημα της γρήγορης επίλυσης, καθώς το POMDP μοντέλο είναι ήδη περιπλεγμένο αυτό καθ’αυτό. Οι ερευνητές, λοιπόν, εστιάζουν την προσοχή τους στην ανάπτυξη γρήγορων αλγορίθμων που να μπορούν να λύνουν αυτά τα προβλήματα σε ρεαλιστικές καταστάσεις. Αρχικά, θα εισάγουμε τις βασικές έννοιες και πληροφορίες προκειμένου να γίνει κατανοητό το MDP μοντέλο και θα συνεχίσουμε με το POMDP που επεκτείνει το προηγόυμενο, κάνοντάς το ρεαλιστικά εφαρμόσιμο. Έπειτα, γίνεται η παρουσίαση του προβλήματος της αυτοματοποιημένης άμυνας σαν POMDP και καταλήγουμε στον αλγόριθμο DESPOT, που είναι από τους καλύτερους που μπορούν να ανταπεξέλθουν σε POMDP προβλήματα τέτοιας κλίμακας.In recent years, artificial intelligence becomes all the more significant for our lives with many applications most of us would not even imagine. Representing the real world demands sophisticated models, which we “feed” to agents to see how they will respond. This is where Markov Decision Processes (MDPs) and Partially Observed Markov Decision Processes (POMDPs) shine. POMDPs provide us with a general framework to depict many different kinds of problems. The capabilities seem endless; from agents that play games optimally to driverless cars. One of these problems that is becoming more and more relevant today is the dynamic defense of a cyber network, which basically means a network that protects itself from intruders in real time by trying to predict their moves and stop them from progressing further into the network and reaching vital points. The development of such a defense system is complicated, since the attackers do not use simplistic methods, but instead rely on a complex sequence of exploits, combining many vulnerabilities. The POMDP model can provide a quite realistic representation of this problem. However, as with most demanding problems modeled as such, it is difficult to solve them efficiently due to the complicated structure of the POMDP model itself. Researchers focus on creating sufficient algorithms that can tackle these problems in realistic situations. We will begin with introducing the basic information needed to understand the MDP model and then we continue with the POMDP model which extends the idea to more realistic applications. Then, we can present the formulation of the dynamic defense problem as POMDP and after that we take a look into the DESPOT POMDP solver, which is one of the best algorithms to scale up and cope with such complicated problems

Genome-wide analysis of the maternal-to-zygotic transition in Drosophila primordial germ cells

Background: During the maternal-to-zygotic transition (MZT) vast changes in the embryonic transcriptome are produced by a combination of two processes: elimination of maternally provided mRNAs and synthesis of new transcripts from the zygotic genome. Previous genome-wide analyses of the MZT have been restricted to whole embryos. Here we report the first such analysis for primordial germ cells (PGCs), the progenitors of the germ-line stem cells. Results: We purified PGCs from Drosophila embryos, defined their proteome and transcriptome, and assessed the content, scale and dynamics of their MZT. Transcripts encoding proteins that implement particular types of biological functions group into nine distinct expression profiles, reflecting coordinate control at the transcriptional and posttranscriptional levels. mRNAs encoding germ-plasm components and cell-cell signaling molecules are rapidly degraded while new transcription produces mRNAs encoding the core transcriptional and protein synthetic machineries. The RNA-binding protein Smaug is essential for the PGC MZT, clearing transcripts encoding proteins that regulate stem cell behavior, transcriptional and posttranscriptional processes. Computational analyses suggest that Smaug and AU-rich element binding proteins function independently to control transcript elimination. Conclusions: The scale of the MZT is similar in the soma and PGCs. However, the timing and content of their MZTs differ, reflecting the distinct developmental imperatives of these cell types. The PGC MZT is delayed relative to that in the soma, likely because relief of PGC-specific transcriptional silencing is required for zygotic genome activation as well as for efficient maternal transcript clearance.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000305391700004&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Biotechnology & Applied MicrobiologyGenetics & HereditySCI(E)20ARTICLE2null1

Beyond the physical risk: Psychosocial impact and coping in healthcare professionals during the COVID-19 pandemic

Aims and objectives This study aimed to examine the psychosocial impact and identify risk factors for poor psychosocial outcomes in healthcare professionals during the Coronavirus disease 2019 (COVID-19) pandemic in Cyprus. Background Healthcare professionals are in the forefront of the COVID-19 pandemic facing an unprecedented global health crisis, which can have consequences on their psychosocial health. There is a need to identify risk factors for poor psychosocial outcomes to inform the design of tailored psychological interventions. Design Cross-sectional online study. Methods A total of 1071 healthcare professionals completed self-report questionnaires. Measures included sociodemographic information, COVID-19-related characteristics, quality of life (Brief World Health Organization Quality of Life; WHOQOL-Bref), anxiety (Generalized Anxiety Disorder-7; GAD-7), depression (Patient Health Questionnaire-8; PHQ-8), occupational burnout (Copenhagen Burnout Inventory; CBI), and coping (Brief Coping Orientation to Problems Experienced; Brief COPE). This article follows the STROBE reporting guidelines. Results The prevalence of moderate to severe anxiety and clinically significant depression was 27.6% and 26.8%, respectively. Significant risk factors for poor psychological outcomes included being female, being a nurse or doctor (vs non-medical professional), working in frontline units (inpatient, intensive care), perceptions of inadequate workplace preparation to deal with the pandemic, and using avoidance coping. Depression and occupational burnout were significant risk factors for poor quality of life. Conclusion The findings suggest several individual, psychosocial, and organisational risk factors for the adverse psychological outcomes observed in healthcare professionals during the COVID-19 pandemic. Relevance to clinical practice This study highlights the urgent need for screening for anxiety and depression and psychological interventions to combat an imminent mental health crisis in healthcare professionals during the COVID-19 pandemic. Pandemic response protocols and public health initiatives aiming to improve and prevent mental health problems in healthcare professionals during the current and future health crises, need to account for the various factors at play

Exploring the factors associated with the mental health of frontline healthcare workers during the COVID-19 pandemic in Cyprus

Introduction: The spread of COVID-19 into a global pandemic has negatively affected the mental health of frontline healthcare-workers. This study is a multi-centre, cross-sectional epidemiological study that uses nationwide data to assess the prevalence of stress, anxiety, depression and burnout among health care workers managing COVID-19 patients in Cyprus. The study also investigates the mechanism behind the manifestation of these pathologies, as to allow for the design of more effective protective measures. Methods: Data on the mental health status of the healthcare workers were collected from healthcare professionals from all over the nation, who worked directly with Covid patients. This was done via the use of 64-item, self-administered questionnaire, which was comprised of the DASS21 questionnaire, the Maslach Burnout Inventory and a number of original questions. Multivariable logistic regression models were used to investigate factors associated with each of the mental health measures. Results: The sample population was comprised of 381 healthcare professionals, out of which 72.7% were nursing staff, 12.9% were medical doctors and 14.4% belonged to other occupations. The prevalence of anxiety, stress and depression among the sample population were 28.6%, 18.11% and 15% respectively. The prevalence of burnout was 12.3%. This was in parallel with several changes in the lives of the healthcare professionals, including; working longer hours, spending time in isolation and being separated from family. Discussion: This study indicates that the mental health of a significant portion of the nation’s workforce is compromised and, therefore, highlights the need for an urgent intervention particularly since many countries, including Cyprus, are suffering a second wave of the pandemic. The identified risk factors should offer guidance for employers aiming to protect their frontline healthcare workers from the negative effects of the COVID-19 pandemic