Semantic Interpretation and Validation of Graph Attention-based Explanations for GNN Models

In this work, we propose a methodology for investigating the use of semantic attention to enhance the explainability of Graph Neural Network (GNN)-based models. Graph Deep Learning (GDL) has emerged as a promising field for tasks like scene interpretation, leveraging flexible graph structures to concisely describe complex features and relationships. As traditional explainability methods used in eXplainable AI (XAI) cannot be directly applied to such structures, graph-specific approaches are introduced. Attention has been previously employed to estimate the importance of input features in GDL, however, the fidelity of this method in generating accurate and consistent explanations has been questioned. To evaluate the validity of using attention weights as feature importance indicators, we introduce semantically-informed perturbations and correlate predicted attention weights with the accuracy of the model. Our work extends existing attention-based graph explainability methods by analysing the divergence in the attention distributions in relation to semantically sorted feature sets and the behaviour of a GNN model, efficiently estimating feature importance. We apply our methodology on a lidar pointcloud estimation model successfully identifying key semantic classes that contribute to enhanced performance, effectively generating reliable post-hoc semantic explanations.Comment: International Conference on Advanced Robotics (ICAR 2023

Semantic interpretation and validation of graph attention-based explanations for GNN models

In this work, we propose a methodology for investigating the use of semantic attention to enhance the explainability of Graph Neural Network (GNN)-based models. Graph Deep Learning (GDL) has emerged as a promising field for tasks like scene interpretation, leveraging flexible graph structures to concisely describe complex features and relationships. As traditional explainability methods used in eXplainable AI (XAI) cannot be directly applied to such structures, graph-specific approaches are introduced. Attention has been previously employed to estimate the importance of input features in GDL, however, the fidelity of this method in generating accurate and consistent explanations has been questioned. To evaluate the validity of using attention weights as feature importance indicators, we introduce semantically-informed perturbations and correlate predicted attention weights with the accuracy of the model. Our work extends existing attention-based graph explainability methods by analysing the divergence in the attention distributions in relation to semantically sorted feature sets and the behaviour of a GNN model, efficiently estimating feature importance. We apply our methodology on a lidar pointcloud estimation model successfully identifying key semantic classes that contribute to enhanced performance, effectively generating reliable post-hoc semantic explanations

Accountancy graduates' employability: narrowing the gap between employers' expectations and students' perceptions - the role of H.E.

The purpose of this research was to examine the necessary employability skills accountancy graduates are required to possess by exploring employers’ and students’ perceptions, against the backdrop of the prolonged financial crisis in Greece since 2009, and record-high graduate unemployment rates. From this, the study sought to understand how the two groups saw graduate student employability being developed as part of an accountancy and finance degree programme, and their transferability to the workplace. A focal point of concern in the study, was that of the value of an accounting and finance degree in relation to employability, which has never been fully investigated. The research adopted an interpretive qualitative approach. Thematic analysis was used to interpret data obtained from interviewing 30 students with an accounting and finance background from four different universities situated in Athens and Piraeus, and a sample of 12 employers across a wide range of industries (including the Big-4). The findings of this study provide support that employability, more for employers and less for students, was influenced by a variety of personal attributes and situational contextual factors, and was not simply about possessing certain generic skills which has so vastly dominated literature over the past years. To that end, a reframing of the factors that enhance accountancy graduate employability has been proposed, drawing from on a number of conceptual models in the existing literature and by the findings of the study. The study also contributes to the growing discussions regarding the general role of higher education in developing the necessary skills and attributes accounting graduates will require for the profession. Twenty percent of the student cohort were working in a relevant accountancy position at the time of the interview, and the analysis of the results shows that there were marked differences in the cohort’s perceptions, between students and those that had graduated and were in a working position. This suggests that a longitudinal qualitative research study could be a sound basis for future research in order to explore whether the working environment influences the perceptions of students as they transition into their job roles resulting in their opinions changing.The American College of Greece - Dere

Infectious Endocarditis complicated by embolic events, clinical case

(Coordonatori ştiinţifici: Alexandra Grejdieru, dr, conf. univ., Liviu Grib, d.h.ş.m., prof. univ.) Departamentul Medicină Internă. Disciplina Cardiologie. Clinica Medicală nr.3, USMF „Nicolae Testemiţanu”Infectious Endocarditis (IE) is a septic disease, with cardiac damage manifested by vegetations, leading to structural impairment of the heart and systemic embolism. Its incidence is 1.9 to 6.2 cases per 100,000 persons / year, mortality rate ranging from 20 to 25% [1,4,5]. Embolic complications are common signs and relevant prediction factors in patients with IE. They are revealed in 22-43% of cases with IE and cause high mortality rate and early invalidity [1,6,7]. It was found that the highest embolization rate was in IE caused by Staphylococcus aureus, Candida and HACEK group of microorganisms, in patients with large floating vegetation, located on aortic and mitral valve [2,3,6]. The authors present a clinical case of the patient P. 35, intravenous drug user with IE and embolic events involving the pulmonary artery branches, with anticardiolipin antibodies (ACA) in titer of 32 GPL. Detection of high titres of ACA in a patient with IE increases likelihood of developing embolic complications [8]. Endocardita infecţioasă (EI) este o maladie septică, ce se manifestă prin leziuni cardiace vegetante, care determină deteriorări structurale şi embolii sistemice. Incidenţa EI este de 1,9 – 6,2 cazuri la 100000 persoane/an, mortalitatea fiind de 20 – 25 % [1,4,5]. Complicaţiile embolice se dezvoltă în 22 – 43% cazuri şi constituie una din cauzele mortalităţii înalte şi invalidizării precoce [1,6,7]. S-a constatat că cea mai înaltă rată a embolizărilor este înregistrată în EI provocată de Staphylococcus aureus, Candida şi microorganismelor din grupul HACEK la pacienţii cu vegetaţii de dimensiuni mari, flotante, localizate pe valva aortică şi mitrală [2,3,6]. Autorii prezintă un caz clinic a pacientei P. de 35 ani, UDIV cu EI şi evenimente embolice în ramurile arterei pulmonare, cu anticorpi anticardiolipinici (ACL) în titru 32 GPL. Depistarea titrelor înalte de anticorpi ACL la un pacient cu EI creşte probabilitatea dezvoltării complicaţiilor embolice [8]

SEM-GAT: explainable semantic pose estimation using learned graph attention

This paper proposes a Graph Neural Network (GNN)-based method for exploiting semantics and local geometry to guide the identification of reliable pointcloud registration candidates. Semantic and morphological features of the environment serve as key reference points for registration, enabling accurate lidar-based pose estimation. Our novel lightweight static graph structure informs our attention-based node aggregation network by identifying semantic-instance relationships, acting as an inductive bias to significantly reduce the computational burden of pointcloud registration. By connecting candidate nodes and exploiting cross-graph attention, we identify confidence scores for all potential registration correspondences and estimate the displacement between pointcloud scans. Our pipeline enables introspective analysis of the model’s performance by correlating it with the individual contributions of local structures in the environment, providing valuable insights into the system’s behaviour. We test our method on the KITTI odometry dataset, achieving competitive accuracy compared to benchmark methods and a higher track smoothness while relying on significantly fewer network parameters

Types of Mycoplasma pneumoniae in Greece

Throat swab specimens were obtained during 2003 from Greek hospitalized children with acute respiratory tract infection. In order to type M. pneumoniae strains a partial region of P1 gene was amplified directly from the clinical specimens and sequence variations among the strains were investigated. It was found that predominant was M. pneumoniae type 1

SEM-GAT: Explainable Semantic Pose Estimation using Learned Graph Attention

This paper proposes a Graph Neural Network(GNN)-based method for exploiting semantics and local geometry to guide the identification of reliable pointcloud registration candidates. Semantic and morphological features of the environment serve as key reference points for registration, enabling accurate lidar-based pose estimation. Our novel lightweight static graph structure informs our attention-based node aggregation network by identifying semantic-instance relationships, acting as an inductive bias to significantly reduce the computational burden of pointcloud registration. By connecting candidate nodes and exploiting cross-graph attention, we identify confidence scores for all potential registration correspondences and estimate the displacement between pointcloud scans. Our pipeline enables introspective analysis of the model's performance by correlating it with the individual contributions of local structures in the environment, providing valuable insights into the system's behaviour. We test our method on the KITTI odometry dataset, achieving competitive accuracy compared to benchmark methods and a higher track smoothness while relying on significantly fewer network parameters.Comment: International Conference on Advanced Robotics (ICAR 2023

Is Routine Ultrasound Examination of the Gallbladder Justified in Critical Care Patients?

Objective. We evaluated whether routine ultrasound examination may illustrate gallbladder abnormalities, including acute acalculous cholecystitis (AAC) in the intensive care unit (ICU). Patients and Methods. Ultrasound monitoring of the GB was performed by two blinded radiologists in mechanically ventilated patients irrespective of clinical and laboratory findings. We evaluated major (gallbladder wall thickening and edema, sonographic Murphy's sign, pericholecystic fluid) and minor (gallbladder distention and sludge) ultrasound criteria. Measurements and Results. We included 53 patients (42 males; mean age 57.6 ± 2.8 years; APACHE II score 21.3 ± 0.9; mean ICU stay 35.9 ± 4.8 days). Twenty-five patients (47.2%) exhibited at least one abnormal imaging finding, while only six out of them had hepatic dysfunction. No correlation existed between liver biochemistry and ultrasound results in the total population. Three male patients (5.7%), on the grounds of unexplained sepsis, were diagnosed with AAC as incited by ultrasound, and surgical intervention was lifesaving. Patients who exhibited ≥2 ultrasound findings (30.2%) were managed successfully under the guidance of evolving ultrasound, clinical, and laboratory findings. Conclusions. Ultrasound gallbladder monitoring guided lifesaving surgical treatment in 3 cases of AAC; however, its routine application is questionable and still entails high levels of clinical suspicion