An improved cosmological parameter inference scheme motivated by deep learning

Dark matter cannot be observed directly, but its weak gravitational lensing slightly distorts the apparent shapes of background galaxies, making weak lensing one of the most promising probes of cosmology. Several observational studies have measured the effect, and there are currently running, and planned efforts to provide even larger, and higher resolution weak lensing maps. Due to nonlinearities on small scales, the traditional analysis with two-point statistics does not fully capture all the underlying information. Multiple inference methods were proposed to extract more details based on higher order statistics, peak statistics, Minkowski functionals and recently convolutional neural networks (CNN). Here we present an improved convolutional neural network that gives significantly better estimates of $\Omega_m$ and $\sigma_8$ cosmological parameters from simulated convergence maps than the state of art methods and also is free of systematic bias. We show that the network exploits information in the gradients around peaks, and with this insight, we construct a new, easy-to-understand, and robust peak counting algorithm based on the 'steepness' of peaks, instead of their heights. The proposed scheme is even more accurate than the neural network on high-resolution noiseless maps. With shape noise and lower resolution its relative advantage deteriorates, but it remains more accurate than peak counting

Examination of mechanical and medical application properties of coronary stents

The medical application properties of coronary stents describe their behaviour in the human vascular system from planting to functioning. However these properties have great importance to surgeons, not all of them have standardized examination methods. In our study we demonstrate three procedures, which can be used to examine stents, considering the referred properties, like flaring, trackability and MSA (metallic surface area). In the course of our research four stents were investigated, three made of tube and one made of wire, and the results were promising about the application of these methods described in the followings

Different properties of coronary stents

Stents are mesh structured implants which are used to support the vessel wall in the balloon expanded vessel part. Several methods were developed and applied for the determination of mechanical properties of coronary stents, as a part of a complex pre-clinical in vitro diagnostic system: radiopacity, flaring, metallic surface area and fatigue tests. Three pieces of equipment were assembled for the examination of fatigue properties. The first method simulates the bending stress in the coronary arteries; the second method simulates the effect of the cylindrical mechanical strain which is equivalent to the systolic and diastolic pulse in the coronary arteries; and the third method is using the energy of the ultrasound concentrating to the stent. After fatigue tests stereomicroscopy, optical microscopy, scanning electron microscopy were used for the determination of surface quality and condition. The most frequent failures were scratches, pits and small shrinkage of materials originated from the manufacturing and finishing processes. Because of fatigue tests slip lines occurred in the critical curves, grain boundaries were outlined, the surface of the struts become rough, but these mutations do not affect the functionality of the stents

Az agrár-biotechnológiai szektor társadalmi legitimációs stratégiái Magyarországon = Strategies of the Hungarian Agro-biotechnology Sector Targeted to Social Legitimacy

Feltártuk a hazai szervezeti mezőt, azonosítottuk tagjait, a mező dinamikáját, a tagoknak az agrárbiotechnológiára vonatkozó legitimációs és delegitimációs érvkészletét. Kvalitatív kutatási módszertanra, valamint a legitimáció szervezettudományi irodalmára építettünk, különös tekintettel az érintettek részvételére, piaci és nem piaci stratégiákra. Értékeltük az agrárbiotechnológiára vonatkozó mikro- (vállalati, gazdálkodói), mezo- (iparági) és makro-szintű (kormányzati) érvelésmódot, döntéshozatalt. A szervezeti mező öt kiemelt jellemzője: Statikus bipolaritás, avagy az észlelés csapdája: A mező tagjai két állandósult szekértáborként írják le a terepet, miközben öndefiníciójuk szétfeszíti e határokat. Érvtérkép: Összetett és gazdag érvrendszer, ám az egészségügyi, társadalmi-szociológiai vonatkozások relatív hiánya. Központi érv: versenyképesség és/vagy visszafordíthatatlan, ismeretlen környezeti kockázatok. Egyéni szint: Nem érintett csoportokban, hanem személyekben testesül meg a szervezeti mező, így kiemelt hangsúlyt kap az egyének kompetenciája, személyes integritása, kapcsolatai. Központi, marginális tagok: kutatók, mint a diskurzus leginkább látható szereplői; fogyasztók, gazdák bevonása legfeljebb érintőleges. Eltérő vállalati legitimációs stratégiák, taktikák: a legitimáció befolyásolásának különböző háttere (helyi beágyazottság, termékkör, piaci pozíció stb.) és eszköztára (kommunikációs nyitottság és kitettség, proaktivitás-kivárás stb). | Organizational field of agri-biotechnology in Hungary is explored: members and field dynamics are identified, legitimacy and de-legitimacy argumentations of field members are depicted. Research was built on qualitative research methodologies and organizational literature of legitimacy with a special emphasis on stakeholder participation, market and non-market strategies. Micro (corporate and farmer), mezo (industry) and macro (governmental) level argumentation and decision-making on agri-biotechnology have been analysed. Five highlighted features of the organizational field: Static bipolarity, i.e. perception trap: members of the field draw a picture of two distinct groups of opponents. Self-definition of several members do not match this bipolarity. Argumentation map: Complex and rich argumantation, health and social aspects are realitvely neglected. Central argument: competitiveness and/or irreversible, unknown environmetal risks. Personal characterisitics: the field is basically constituted by individuals - and not stakeholder groups or organizations -, thus special focus is pledged to personal knowledge, integrity, relations. Central, marginal members: Researchers as the most visible, consumers and farmers as the least involved. Diverse corporate legitimacy strategies, tactics: variant backgrounds (local embeddedness, product range, market position, etc.) and tools (oppenness in and exposure to communication, proactive vs wait-and-see approach, etc.

Weak lensing cosmology with convolutional neural networks on noisy data

Weak gravitational lensing is one of the most promising cosmological probes of the late universe. Several large ongoing (DES, KiDS, HSC) and planned (LSST, EUCLID, WFIRST) astronomical surveys attempt to collect even deeper and larger scale data on weak lensing. Due to gravitational collapse, the distribution of dark matter is non-Gaussian on small scales. However, observations are typically evaluated through the two-point correlation function of galaxy shear, which does not capture non-Gaussian features of the lensing maps. Previous studies attempted to extract non-Gaussian information from weak lensing observations through several higher-order statistics such as the three-point correlation function, peak counts or Minkowski-functionals. Deep convolutional neural networks (CNN) emerged in the field of computer vision with tremendous success, and they offer a new and very promising framework to extract information from 2 or 3-dimensional astronomical data sets, confirmed by recent studies on weak lensing. We show that a CNN is able to yield significantly stricter constraints of ($\sigma_8, \Omega_m$) cosmological parameters than the power spectrum using convergence maps generated by full N-body simulations and ray-tracing, at angular scales and shape noise levels relevant for future observations. In a scenario mimicking LSST or Euclid, the CNN yields 2.4-2.8 times smaller credible contours than the power spectrum, and 3.5-4.2 times smaller at noise levels corresponding to a deep space survey such as WFIRST. We also show that at shape noise levels achievable in future space surveys the CNN yields 1.4-2.1 times smaller contours than peak counts, a higher-order statistic capable of extracting non-Gaussian information from weak lensing maps

A Környezetvédelmi Természetvédelmi és Vízügyi Felügyelőségek (KTVF) mint street-level bureaucracy szerepe a magyar környezetpolitikában = The Role of Environmental Inspectorates as Street-level Bureaucrats in the Implementation of Environmental Policy

A környezetpolitika végrehajtási intézményrendszere jelentős átalakításokon ment át az elmúlt évtizedben Magyarországon. A változtatások érintették az intézményrendszer szervezeti felépítését és feladatait egyaránt. E változások a street-level bürokraták munkakörülményeit is jelentősen átírták. Kutatásunkban a nemzetközi eredmények tükrében a leíró közpolitika elemzés módszereit alkalmazva bemutattuk a környezetpolitika végváraiban dolgozó felügyelők munkakörülményeit, a végrehajtás folyamatát és annak buktatóit. A végrehajtás belső tényezőiként igen fontos szerepet játszanak a végrehajtásban az intézményi tanulás folyamatai, és a kollégák belső munkakörülményei, feladatkörei. Ennek eredményeként meghatározó a felügyelő szaktudása, magabiztossága és infrastrukturális ellátottsága. A külső tényezők között a jogszabályok minősége, értelmezhetősége, a társhatósági kapcsolatok és a különböző ügyféltípusok a meghatározóak. | Environmental policy implementation has undergone major restructuring in the last decade in Hungary. These changes influenced both the structure of institutions and the scope of duties and significantly affected the working conditions of street-level bureaucrats. The present research analysed in detail the working conditions of street-level bureaucrats in Hungarian environmental policy comparing them to those of some of their Western counterparts applying a descriptive ex-post public policy analysis framework. It also analysed the process of environmental policy implementation highlighting major implementation gaps. Determining internal factors of policy implementation like organisational learning processes, working conditions and duties are playing important roles. As a result of this the knowledge of inspectors, their confidence and infrastructural endowments are decisive factors. Among external factors the quality and equivalence of legal documents, relations to co-authorities and the types of clients are the most important

An improved cosmological parameter inference scheme motivated by deep learning

Dark matter cannot be observed directly, but its weak gravitational lensing slightly distorts the apparent shapes of background galaxies, making weak lensing one of the most promising probes of cosmology. Several observational studies have measured the effect, and there are currently running(1,2) and planned efforts(3,4) to provide even larger and higher-resolution weak lensing maps. Owing to nonlinearities on small scales, the traditional analysis with two-point statistics does not fully capture all of the underlying informations(5). Multiple inference methods have been proposed to extract more details based on higher-order statistics(6,7), peak statisticss(8-13), Minkowski functionals(14-16) and recently convolutional neural networks(17,18). Here we present an improved convolutional neural network that gives significantly better estimates of the Omega(m) and sigma(8) cosmological parameters from simulated weak lensing convergence maps than state-of-art methods and that is also free of systematic bias. We show that the network exploits information in the gradients around peaks, and with this insight we have constructed an easy-to-understand and robust peak-counting algorithm based on the steepness of peaks, instead of their heights. The proposed scheme is even more accurate than the neural network on high-resolution noiseless maps. With shape noise and lower resolution, its relative advantage deteriorates, but it remains more accurate than peak counting

Részvételi akciókutatással a társadalmi kirekesztés ellen: egy szegedi példa tanulságai = Participatory action research and social integration: conclusions of a case study in Szeged

Large international surveys and regional and national (Hungarian) examinations all show that the vast majority of the European and Hungarian Roma population belongs to the most disadvantaged groups of society. Furthermore, social disadvantages and spatial segregation are often connected. Our paper is based on a case study carried out in Szeged (Hungary) in relation to the problems mentioned. In Szeged, university researchers and social activists have been working together with local Roma leaders and Roma families living in segregated areas (segregates) from the beginning of 2011 within the framework of participatory action research. As a participatory action research-type research process our cooperation has two strongly interconnected goals. First, we aim to generate valuable knowledge about the social integration of Roma. Second, we aim to contribute to local social integration processes by testing our knowledge in practice through actions in the field. Based on our ongoing work, we gained valuable research experience regarding local social segregation and integration and its spatial aspects, and the role of scientific research and researchers in this area. In our study we give an overview of these experiences. Besides introducing some general questions and (ethical) dilemmas regarding social science research dealing with social segregation/integration, we examine how social and spatial integration are interconnected and the requirements social scientists have to meet if they deal with social integration issues within the framework of participatory action research. Our conclusions are that participatory action research sets new challenges for social researchers – because of its action component, for example – and participatory action research dealing with local social integration of the Roma might cause “objective”, “outsider” or “independent” researchers turning into local political actors. However, this “turn” does not necessarily mean a real change in the role of experts, but it rather means that interests and values necessarily appearing behind scientific research are made explicit in the process of participatory action research

Backcasting for Sustainable Employment

Sustainability and employment are terms seldom used together. Especially when defining sustainability in the stricter sense of the word, delineating a world where strong sustainability is the norm, it is problematic to deduct which elements may compose sustainable employment. In the relevant discourse, two distinct directions can be identified. Ecological modernization promises ―quick fixes‖ to employment problems while reducing environmentally harmful economic activities without initiating major changes either in our ways of thinking or in our way of living. At the same time, the radical change paradigm disposes of the concepts of the free market society and believes that new ―great transformations‖ are unavoidable, whereby values must change just as much as institutions. Yet, how far have these normative theoretical approaches penetrated our everyday thinking? The paper builds upon the experience of a backcasting project on sustainable employment conducted in Hungary in 2012 and early 2013 and suggests that when people are given the chance to leave the path dependencies of today behind and imagine a sustainable future, their normative visions provide us with invaluable insight as to what may constitute sustainable employment. It also contributes towards our understanding of which policy tools lead us towards a more sustainable world of work in particular and a more sustainable society in general