Exploring the Performance and Efficiency of Transformer Models for NLP on Mobile Devices

Deep learning (DL) is characterised by its dynamic nature, with new deep neural network (DNN) architectures and approaches emerging every few years, driving the field's advancement. At the same time, the ever-increasing use of mobile devices (MDs) has resulted in a surge of DNN-based mobile applications. Although traditional architectures, like CNNs and RNNs, have been successfully integrated into MDs, this is not the case for Transformers, a relatively new model family that has achieved new levels of accuracy across AI tasks, but poses significant computational challenges. In this work, we aim to make steps towards bridging this gap by examining the current state of Transformers' on-device execution. To this end, we construct a benchmark of representative models and thoroughly evaluate their performance across MDs with different computational capabilities. Our experimental results show that Transformers are not accelerator-friendly and indicate the need for software and hardware optimisations to achieve efficient deployment.Comment: Accepted at the 3rd IEEE International Workshop on Distributed Intelligent Systems (DistInSys), 202

Use and assessment of remote sensing for the safety of maritime shipping

Αντικείμενο της εργασίας είναι η εφαρμογή της δορυφορικής τηλεπισκόπησης για τον υπολογισμό και την εκτίμηση φυσικών παραμέτρων συνδεόμενων με κινδύνους για τη ναυτιλία. Ειδικότερα, μέσω της χρήσης δορυφορικών εικόνων σε διάφορες φασματικές περιοχές, θα εξαχθούν οι κατάλληλες παράμετροι, ώστε να μελετηθεί η κίνηση των θαλασσίων ρευμάτων, η μεταβλητότητα στην παγοκάλυψη σε διαύλους ναυσιπλοΐας, ο εντοπισμός πετρελαιοκηλίδων και η παρουσία επικίνδυνων φορτίων. Στο πλαίσιο της εργασίας, θα διαμορφωθεί η εργαλειοθήκη που θα συμβάλει στην ασφάλεια της ναυσιπλοΐας και θα αξιολογηθεί η εφαρμοστικότητά της και η δυνατότητα επιχειρησιακής χρήσης, βάσει των διαθέσιμων δεδομένων και μελλοντικών δορυφορικών αποστολών.The scope of this work is the implementation of satellite remote sensing for the calculation and estimation of physical parameters associated with risk for maritime shipping. In particular, through the use of satellite imagery in different spectral regions and the exploitation of the advantages of passive and active remote sensing, the appropriate parameters will be extracted, in order to study the wind speed and direction, the variability of sea ice coverage in marine channels, oil spillages and the presence of dangerous cargoes. As part of the work, Sentinel Application Platform (SNAP) and QGIS will be configured, which based on satellites observations, will contribute to marine navigation. Finally, the thesis will evaluate the applicability of the toolbox to business function depending on the available satellite data and the future satellite missions

MgcRacGAP interacts with HIF-1 alpha and regulates its transcriptional activity

HIF-1 alpha is the inducible subunit of the dimeric transcription factor HIF-1 (Hypoxia Inducible Factor 1). It is induced by hypoxia and hypoxia-mimetics in most cell types, as well as non-hypoxic signals such as growth factors, cytokines and oncogenes, often in a cell specific manner. HIF-1 is present in virtually all cells of higher eukaryotes and its function is of great biomedical relevance since it is highly involved in development, tumor progression and tissue ischemia. Intracellular signaling to HIF-1 alpha, as well as its further action, involves its participation in numerous protein complexes. Using the yeast two-hybrid system we have identified MgcRacGAP (male germ cell Rac GTPase Activating Protein) as a HIF-1 alpha interacting protein. The MgcRacGAP protein is a regulator of Rho proteins, which are principally involved in cytoskeletal organization. We have verified specific binding of HIF-1 alpha and MgcRacGAP in vitro and in vivo in mammalian cells. We have additionally shown that MgcRacGAP overexpression inhibits HIF-1 alpha transcriptional activity, without lowering HIF-1 alpha protein levels, or altering its subcellular localization. Moreover, this inhibition is dependent on the MgcRacGAP domain that interacts with HIF-1 alpha. In conclusion, our findings demonstrate that HIF-1 alpha function is negatively affected by its interaction with MgcRacGAP. Copyright (C) 2007 S. Karger AG, Basel