Az erkölcstan tantárgy útkeresése

Egy társadalom fennmaradásának nélkülözhetetlen feltétele a kiszámítható értékrendre épülő együttműködés, amelynek alapja az erkölcsi viszonyrendszer ismerete. Erkölcsön a cselekedetek megítélésénél mértékadó értékek, szabályok és szokások azon együttesét értjük, amelyek meghatározzák az emberek gondolkodásmódját, magatartását és cselekedeteit: a résztvevők ezek alapján tudják, hogy társadalmi értelemben mi a „jó” és mi a „rossz”, illetve tudják, hogy mennyiben számíthatnak társaik segítségére, elfogadására. Az erkölcs szempontjai gyakorlati életre irányulnak, közvetlen útmutatást tartalmaznak arra nézve, hogy miként cselekedjünk. Az erkölcsi nevelés alapja az erkölcsi érzék kifejlesztése, amelyet a szocializáció során készség, hajlam és érzület formájában sajátítunk el. Nem velünk született, hanem tanult magatartásról van szó, de olyanról, amely személyiségünk alapszerkezetéhez tartozik, és nagyfokú állandóságot mutat. Az erkölcsi érzék formálható és elmélyíthető, de csak a gyakorlat által. Elméletben nem lehet valaki erkölcsös. A nagylelkűség adakozóvá, az önzés zsugorivá tesz, a szelíd bánásmód kíméletre szoktat, az erőszak eldurvít. Az erkölcsi készségek kiművelését az erkölcsi cselekvés feltételeinek tudatosítása előnyösen befolyásolja. Ugyanakkor erkölcsi érzék híján az erkölcsi fogalmak elsajátítása önmagában még nem tesz erkölcsössé

Improper Supplementation Habits of Folic Acid Intake by Hungarian Pregnant Women: Improper Recommendations

Background: Neural tube defects (NTDs) are some of the most common congenital anomalies. Proper folic acid supplementation is a dominant risk factor, which has been shown to decrease the incidence of NTDs. In Canada, the incidence of neuroblastoma has presented a considerable decrease of 60% as a result of enrichment cereal grain flours with synthetic folic acid. The aim of this study was to investigate the effect of folic acid intake by pregnant women on the incidence of NTDs and neuroblastoma. Methods: Regular folic acid intake has been recommended to pregnant women in Hungary since the eighties of the last century by health visitors eventually raking effect as an official protocol which had been released in 1997. During 2001, 2002 and 2003. folic acid intake habits of pregnant women were evaluated by health visitors, proving to be successful in collecting data front 95.06% of the pregnant women. The incidence of NTDs has been registered by the Hungarian National Centre of Epidemiology, Department of Human Genetics and Teratology. The Pediatric Cancer Registry provided the incidence of neuroblastoma in children. Results: Consistent findings revealed a regular intake of supplementary folic acid products by 68.71% of the pregnant women. Out of these. 93.13% of pregnant women who were taking folic acid, started the supplementation after their 7 weeks of pregnancies, a time designated as the completion period of the development of the neural tube. The dose of folic acid supplementation was evaluated as less than 5 mg/day in 84.75% of the pregnant women. In Hungary, the incidence of NTDs has remained constant, while the incidence of neuroblastoma has shown constant slight increase in spite of the introduction of folic acid supplementation in 1997. Conclusions: Based on our experience, folic acid supplementation was initiated after the recognition of pregnancy and its application in a dose of lower than 5 mg/day neither decreased the incidence of NTDs nor did it have an effect on the neuroblastoma incidence. It is implicated that proper folic acid supplementation, which is started front the conception. can be achieved only with the enrichment of cereal grain flours

Self-supervised Learning of Interpretable Keypoints from Unlabelled Videos

We propose KeypointGAN, a new method for recognizing the pose of objects from a single image that for learning uses only unlabelled videos and a weak empirical prior on the object poses. Video frames differ primarily in the pose of the objects they contain, so our method distils the pose information by analyzing the differences between frames. The distillation uses a new dual representation of the geometry of objects as a set of 2D keypoints, and as a pictorial representation, i.e. a skeleton image. This has three benefits: (1) it provides a tight `geometric bottleneck' which disentangles pose from appearance, (2) it can leverage powerful image-to-image translation networks to map between photometry and geometry, and (3) it allows to incorporate empirical pose priors in the learning process. The pose priors are obtained from unpaired data, such as from a different dataset or modality such as mocap, such that no annotated image is ever used in learning the pose recognition network. In standard benchmarks for pose recognition for humans and faces, our method achieves state-of-the-art performance among methods that do not require any labelled images for training.Comment: CVPR 2020 (oral). Project page: http://www.robots.ox.ac.uk/~vgg/research/unsupervised_pose

DOVE: learning deformable 3D objects by watching videos

Learning deformable 3D objects from 2D images is often an ill-posed problem. Existing methods rely on explicit supervision to establish multi-view correspondences, such as template shape models and keypoint annotations, which restricts their applicability on objects “in the wild”. A more natural way of establishing correspondences is by watching videos of objects moving around. In this paper, we present DOVE, a method that learns textured 3D models of deformable object categories from monocular videos available online, without keypoint, viewpoint or template shape supervision. By resolving symmetry-induced pose ambiguities and leveraging temporal correspondences in videos, the model automatically learns to factor out 3D shape, articulated pose and texture from each individual RGB frame, and is ready for single-image inference at test time. In the experiments, we show that existing methods fail to learn sensible 3D shapes without additional keypoint or template supervision, whereas our method produces temporally consistent 3D models, which can be animated and rendered from arbitrary viewpoints. Project page: https://dove3d.github.io/

Developments in Hungarian Labour and Public Service Legislation during the 2011–2012 Codification and the Subsequent Comprehensive Amendments

The study deals with the second codification wave similar to and following the first codification period of labor and public service law after the change of regime in 1992-1993, which took place because contrary to the previous left-liberal government policy, a very different, right-wing civilian government came to power in the parliamentary elections of 2010. The article in the previous English volume showed in detail only the employment and public service legislation of 1992-1993, while the second codification of 2011-2012 relating to these two fields of law was only outlined. In this writing we give a more profound critical analysis of the re-codification of employment and public service law in 2012 and the subsequent amendments. Our study covers both individual labor and public service law, as well as employment and public service law relations in both the fields of labor law and public service law

DOVE: learning deformable 3D objects by watching videos

Learning deformable 3D objects from 2D images is often an ill-posed problem. Existing methods rely on explicit supervision to establish multi-view correspondences, such as template shape models and keypoint annotations, which restricts their applicability on objects “in the wild”. A more natural way of establishing correspondences is by watching videos of objects moving around. In this paper, we present DOVE, a method that learns textured 3D models of deformable object categories from monocular videos available online, without keypoint, viewpoint or template shape supervision. By resolving symmetry-induced pose ambiguities and leveraging temporal correspondences in videos, the model automatically learns to factor out 3D shape, articulated pose and texture from each individual RGB frame, and is ready for single-image inference at test time. In the experiments, we show that existing methods fail to learn sensible 3D shapes without additional keypoint or template supervision, whereas our method produces temporally consistent 3D models, which can be animated and rendered from arbitrary viewpoints. Project page: https://dove3d.github.io/

A szimbiotikus gümő bakteriális inváziójában és a szimbioszóma mükődésében résztvevő növényi gének azonosítása. = Genetic analysis of symbiosome initiation and development in legume nodulation.

A Sinorhizobium meliloti és a Medicago truncatula között kialakuló szimbiotikus nitrogénkötésen hibás növényi mutánsok fenotípusos vizsgálata során megállapítottuk, hogy a 9F és 14S mutánsok a szimbiotikus gümő inváziójában, a többi mutáns a bakteroid átalakulásban vagy pedig a gümő működésében szenvedett hibát. A 9F mutáns mikroszkópos vizsgálata azt is feltárta, hogy a baktériumok az infekciós fonalakból nem voltak képesek lefűződni és a gümő sejtjeibe jutni. A 7Y mutánsban erős autofluoreszcenciát mutató polifenol képződést figyeltünk meg, ami a beinduló patogén válaszreakció eredménye. Megállapítottuk, hogy az 5L és 11S mutánsok egymás alléljai, a 13U a már korábban más kutató csoportok által azonosított dnf5 mutáns, a 6V pedig a dnf7 allélja, többi mutáns pedig különálló komplementációs csoportot képez. Térképezésen alapuló génizolálással klónoztuk a 9F mutánsban hibát szenvedett IPD3 gént. Megállapítottuk, hogy a 9F a mycorrhiza szimbiótikus kapcsolatban is hibát szenvedett, azaz az IPD3 gén a szimbiotikus partnerek befogadásában játszik szerepet. Vizsgálataink kiderítették, hogy a 9F mutánsban a Nod faktor szignálút hibásan működik. Genetikai térképezés és gén chip alapú módszer kombinálásával azonosítottuk az 5L és 11S mutánsokban hibás szulfát transzporter gént. A6V, 12AA és 13U mutánsok esetében is azonosítottuk a fenotípusért felelős deléciókat, genetikai térképezéssel meghatároztuk a 7Y és 14S növényekben hibát szenvedett géneket tartalmazó genomi régiókat. | The phenotypic characterization of the ineffective mutants impaired in the symbiotic interaction between Sinorhizobium meliloti and Medicago truncatula showed that nodules on 9F and 14S mutants were impaired in the invasion of the nodule cells by bacteria. Aberrant infection process was detected in mutant 9F because bacteria were not released from the infection threads. The other mutants showed defects in bacteroid differentiation, displayed disintegration of the symbiotic structures and were impaired in functioning of the symbiotic nodule. Mutant 7Y showed high accumulation of polyphenolic compounds indicating strong defense reaction against rhizobia. We identified allelic relationship between 5L and 11S, 13U and dnf5, 6V and dnf7 fix- mutants. Positional cloning identified that the IPD3 gene is impaired in the 9F mutant. Further characterization showed that the effectiveness of the AM colonization significantly reduced in the 9F mutant indicating that IPD3 functions in the accommodation of the symbiotic partners in the host cells. Expression data suggested that IPD3 acts in the NF signal pathway. Combining genetic mapping and gene chip-based cloning we identified that the SST1 gene was impaired in the 5L and 11S mutants. We also identified the deletions which are probably responsible for the mutant phenotype in the 6V, 12AA and 13U mutants. Map-based cloning experiments also determined the genomic regions containing the mutated Fix genes in 7Y and 14S mutant plants

Instant uncertainty calibration of NERFs using a meta-calibrator

Neural Radiance Fields (NeRFs) have markedly improved novel view synthesis, but accurate uncertainty quantification in their image predictions remains an open problem. The prevailing methods for estimating uncertainty, including the state-of-the-art Density-aware NeRF Ensembles (DANE) [29], quantify uncertainty without calibration. This frequently leads to over- or under-confidence in image predictions, which can undermine their real-world applications. In this paper, we propose a method which, for the first time, achieves calibrated uncertainties for NeRFs. To accomplish this, we overcome a significant challenge in adapting existing calibration techniques to NeRFs: a need to hold out ground truth images from the target scene, reducing the number of images left to train the NeRF. This issue is particularly problematic in sparse-view settings, where we can operate with as few as three images. To address this, we introduce the concept of a meta-calibrator that performs uncertainty calibration for NeRFs with a single forward pass without the need for holding out any images from the target scene. Our meta-calibrator is a neural network that takes as input the NeRF images and uncalibrated uncertainty maps and outputs a scene-specific calibration curve that corrects the NeRF’s uncalibrated uncertainties. We show that the meta-calibrator can generalize on unseen scenes and achieves well-calibrated and state-of-the-art uncertainty for NeRFs, significantly beating DANE and other approaches. This opens opportunities to improve applications that rely on accurate NeRF uncertainty estimates such as next-best view planning and potentially more trustworthy image reconstruction for medical diagnosis