Autonomous Cars – What Lies Behind the Lack of Readiness

Autonomous systems are already available for public and private transport. The necessary hardware and software products have been created, and novel designs for (semi-) autonomous vehicles are launched every year, but their use is limited, and the penetration is not increasing rapidly. While this might be owing to their high price, their perception is also not universally positive. Many are afraid of not only using, but being around them. After introducing the relevant literature on trust in autonomous vehicles and the factors affecting it, the current article presents the data of an international quantitative research of 666 people. It highlights the biggest perceived threats and their prevalence, and also tries to uncover why more than half of the respondents are afraid of autonomous vehicles. In line with the data presented in the article, the topic is gendered – male respondents were more open towards autonomous vehicles. Furthermore, those who are not ready for autonomous vehicles have a generally higher level of fear of potential negative consequences, such as hacker attacks, system malfunctions, or lack of control. On the other hand, those in favour of automated vehicles believe that they have a positive effect on the occurrence of accidents, owing to their heightened reaction speed provided by the sensory system and the computing capacity which is far superior to that of humans, as well as on the society, on carbon emission, and, as a result, on our natural environment. Consequently, autonomous vehicles could form an important element of the transport systems of future smart cities

A csillagfejlődés késői állapotai = Late stages of stellar evolution

Tanulmányoztuk a csillagfejlődés, csillagrezgések és -robbanások asztrofizikájának nyitott kérdéseit hazai, külföldi és űrbéli nagyműszerekkel végzett megfigyelésekkel. Vizsgáltuk pulzáló vörös óriás változócsillagoknál a pulzáció kapcsolatát a csillagfejlődéssel és a periódusváltozások okait. Elvégeztük több mint 300 vörös óriáscsillagnak a Kepler-űrtávcsővel készített fénygörbéje analízisét. A csillagoknál Fourier- és wavelet-analízist alkalmaztunk a periodicitás vizsgálatokhoz. Megvizsgáltunk nagy tömegű szuperóriás csillagokat mint lehetséges szupernóva progenitorokat a robbanás előtt. Szupernóvák távolságát, tömegét, luminozitását és fejlődési állapotát határoztuk meg fotometriai és spektroszkópiai megfigyelések alapján. A ROTSE programmal együttműködve megfigyeltünk és elemeztünk mintegy 50 új szupernóvát a 9,2 m-es HET (USA), a 9,3 m-es SALT távcső (Dél-Afrika) és más távcsövek (Piszkéstető, Baja és Szeged) használatával. A projekt új kutatási irányokat nyitott: vörös óriás csillagok rövid periódusú és kis amplitúdójú oszcillációjának vizsgálata a Kepler adatai alapján, és szupernóva-robbanások széles körű tanulmányozása az University of Texas-szal együttműködve. A kutatás végére 96 publikációnk született (ezek közül 1 MTA doktori és 6 PhD értekezés, 1 Science cikk) az OTKA-szám feltüntetésével, összesen 418 impakt faktorral. Ezekre 2013 közepéig 682 hivatkozás történt az ADS szerint (részletes adatok: http://astro.u-szeged.hu/kutatas/.otka/index.html). | In this project we explored open questions of stellar evolution, oscillations and explosions by taking new observations using ground-based and space telescopes. We studied the relationship of pulsation and stellar evolution in red giant stars and physical background of the period changes. We analyzed the light curves of more than 300 red giant stars measured by the Kepler space telescope. We applied Fourier- and wavelet-analysis to perform the periodicity studies. We studied massive supergiants as supernova progenitors before explosion. We determined the distance, mass, luminosity and evolutionary state of supernovae via photometric and spectroscopic measurements. In collaboration with the ROTSE program we observed and studied about 50 new supernovae using observations taken with the 9.2 m HET telescope (USA), the 9.3 m SALT telescope (South Africa) and other telescopes at Piszkéstető, Baja and Szeged. We extended our studies into new directions: the study of short period and small amplitude oscillations of red giant stars based on Kepler data, and comprehensive investigations of supernova explosions in collaboration with University of Texas. We produced 96 publications (among them 1 HAS DSc thesis and 6 PhD theses, 1 Science paper) with the indication of the OTKA-number, the cumulative impact factor is 418. The current number of citations (June 2013) is 682 according to the ADS data base (details: http://astro.u-szeged.hu/kutatas/.otka/index.html)

EASY-APP : An artificial intelligence model and application for early and easy prediction of severity in acute pancreatitis

Acute pancreatitis (AP) is a potentially severe or even fatal inflammation of the pancreas. Early identification of patients at high risk for developing a severe course of the disease is crucial for preventing organ failure and death. Most of the former predictive scores require many parameters or at least 24 h to predict the severity; therefore, the early therapeutic window is often missed.The early achievable severity index (EASY) is a multicentre, multinational, prospective and observational study (ISRCTN10525246). The predictions were made using machine learning models. We used the scikit-learn, xgboost and catboost Python packages for modelling. We evaluated our models using fourfold cross-validation, and the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and accuracy metrics were calculated on the union of the test sets of the cross-validation. The most critical factors and their contribution to the prediction were identified using a modern tool of explainable artificial intelligence called SHapley Additive exPlanations (SHAP).The prediction model was based on an international cohort of 1184 patients and a validation cohort of 3543 patients. The best performing model was an XGBoost classifier with an average AUC score of 0.81 ± 0.033 and an accuracy of 89.1%, and the model improved with experience. The six most influential features were the respiratory rate, body temperature, abdominal muscular reflex, gender, age and glucose level. Using the XGBoost machine learning algorithm for prediction, the SHAP values for the explanation and the bootstrapping method to estimate confidence, we developed a free and easy-to-use web application in the Streamlit Python-based framework (http://easy-app.org/).The EASY prediction score is a practical tool for identifying patients at high risk for severe AP within hours of hospital admission. The web application is available for clinicians and contributes to the improvement of the model

Dynamics Of The Hippocampus: Multiple Strategies

Single cell, network and population level modeling techniques all have their scope and limits in studying neurodynamic phenomena. Illustrations are given for using all the three techniques for understanding hippocampal dynamic phenomena occurring at different organizational levels. Multicompartmental modeling is used here to systemaically investigate the location-dependent effects of GABA-ergic dendritic inhibiton on the firing pattern of pyramidal cells. A two-level network model is established for describing the formation of place fields. A population model was elaborated for investigating the spatial propagation of synchronized activities in the CA3 region of the hippocampus. KEYWORDS: hippocampus, dendritic inhibition, place fields, spatial propagation 1. INTRODUCTION One of the main intentions of computational neuroscience is to integrate anatomical, physiological, neurochemical and behavioural data by coherent concepts and models [1]. A basic structure for which such an integr..