Making Heimat in the modern world: state, Catholicism, and nature in a Bavarian village community

This thesis is an ethnographic study of ‘Heimat’ (home) in a Bavarian village, and how Heimat is made in relationship with the German nation-state, the Catholic church, and the experience of nature. At a time when the village has lost its previous political and economic significance, local efforts to make Heimat have become vital to regenerate the village community. Major economic and political changes since World War II have led to substantial changes in the village, especially the decline of ‘big families’ and rise of local associations (Vereine) as the main organisational force. Against this historical backdrop, local identities emerge in the tensions and entanglements between state formation and local practice. The political reality of Heimat is defined by the ways in which villagers reveal and bridge oppositions between official and vernacular discourses. Aside from government and state, Catholicism also plays an indispensable role in articulating senses of community in Heimat. The ethics and organisational forms of the Catholic Church offer alternative ideals and institutions to secular ones; they can also provide connections between state and village. Furthermore, villagers’ experience of Heimat at present are crucially expressed in the local idea of ‘returning to nature to heal society’s illnesses.’ This local idiom incorporates contradictory characteristics, as a metaphor of villagers’ investments in and hopes for Heimat itself, and with exclusionist connotations. Nature in this sense is both a source of morality for a society deemed lacking and ultimately beyond human morality, for only nature that is essentially different from human society has the power to heal. The unreachability of this idea of nature is its very strength. Heimat, similarly, operates based on a core paradox: to maintain Heimat, villagers tend to externalise the inherent problems of Heimat to an imagined opposition between the ‘traditional village’ (as Heimat) and the ‘modern city’ (as its ultimate ‘other’, with ethnic diversity). But an analysis of the local dialectical understandings of modern time and the corresponding meanings of Heimat reveal that Heimat is essentially a product of modernity

Cascaded Recurrent Neural Networks for Hyperspectral Image Classification

By considering the spectral signature as a sequence, recurrent neural networks (RNNs) have been successfully used to learn discriminative features from hyperspectral images (HSIs) recently. However, most of these models only input the whole spectral bands into RNNs directly, which may not fully explore the specific properties of HSIs. In this paper, we propose a cascaded RNN model using gated recurrent units (GRUs) to explore the redundant and complementary information of HSIs. It mainly consists of two RNN layers. The first RNN layer is used to eliminate redundant information between adjacent spectral bands, while the second RNN layer aims to learn the complementary information from non-adjacent spectral bands. To improve the discriminative ability of the learned features, we design two strategies for the proposed model. Besides, considering the rich spatial information contained in HSIs, we further extend the proposed model to its spectral-spatial counterpart by incorporating some convolutional layers. To test the effectiveness of our proposed models, we conduct experiments on two widely used HSIs. The experimental results show that our proposed models can achieve better results than the compared models

TcGAN: Semantic-Aware and Structure-Preserved GANs with Individual Vision Transformer for Fast Arbitrary One-Shot Image Generation

One-shot image generation (OSG) with generative adversarial networks that learn from the internal patches of a given image has attracted world wide attention. In recent studies, scholars have primarily focused on extracting features of images from probabilistically distributed inputs with pure convolutional neural networks (CNNs). However, it is quite difficult for CNNs with limited receptive domain to extract and maintain the global structural information. Therefore, in this paper, we propose a novel structure-preserved method TcGAN with individual vision transformer to overcome the shortcomings of the existing one-shot image generation methods. Specifically, TcGAN preserves global structure of an image during training to be compatible with local details while maintaining the integrity of semantic-aware information by exploiting the powerful long-range dependencies modeling capability of the transformer. We also propose a new scaling formula having scale-invariance during the calculation period, which effectively improves the generated image quality of the OSG model on image super-resolution tasks. We present the design of the TcGAN converter framework, comprehensive experimental as well as ablation studies demonstrating the ability of TcGAN to achieve arbitrary image generation with the fastest running time. Lastly, TcGAN achieves the most excellent performance in terms of applying it to other image processing tasks, e.g., super-resolution as well as image harmonization, the results further prove its superiority

Conflict Adaptation in 5-Year-Old Preschool Children: Evidence From Emotional Contexts

This research investigated the individual behavioral and electrophysiological differences during emotional conflict adaptation processes in preschool children. Thirty children (16 girls, mean age 5.44 ± 0.28 years) completed an emotional Flanker task (stimulus-stimulus cognitive control, S-S) and an emotional Simon task (stimulus-response cognitive control, S-R). Behaviorally, the 5-year-old preschool children exhibited reliable congruency sequence effects (CSEs) in the emotional contexts, with faster response times (RTs) and lower error rates in the incongruent trials preceded by an incongruent trial (iI trial) than in the incongruent trials preceded by a congruent trial (cI trial). Regarding electrophysiology, the children demonstrated longer N2 and P3 latencies in the incongruent trials than in the congruent trials during emotional conflict control processes. Importantly, the boys showed a reliable CSE of N2 amplitude when faced with fearful target expression. Moreover, 5-year-old children showed better emotional CSEs in response to happy targets than to fearful targets as demonstrated by the magnitude of CSEs in terms of the RT, error rate, N2 amplitude and P3 latency. In addition, the results demonstrated that 5-year-old children processed S-S emotional conflicts and S-R emotional conflicts differently and performed better on S-S emotional conflicts than on S-R emotional conflicts according to the comparison of the RT-CSE and P3 latency-CSE values. The current study provides insight into how emotionally salient stimuli affect cognitive processes among preschool children

Fuzzy Knowledge Distillation from High-Order TSK to Low-Order TSK

High-order Takagi-Sugeno-Kang (TSK) fuzzy classifiers possess powerful classification performance yet have fewer fuzzy rules, but always be impaired by its exponential growth training time and poorer interpretability owing to High-order polynomial used in consequent part of fuzzy rule, while Low-order TSK fuzzy classifiers run quickly with high interpretability, however they usually require more fuzzy rules and perform relatively not very well. Address this issue, a novel TSK fuzzy classifier embeded with knowledge distillation in deep learning called HTSK-LLM-DKD is proposed in this study. HTSK-LLM-DKD achieves the following distinctive characteristics: 1) It takes High-order TSK classifier as teacher model and Low-order TSK fuzzy classifier as student model, and leverages the proposed LLM-DKD (Least Learning Machine based Decoupling Knowledge Distillation) to distill the fuzzy dark knowledge from High-order TSK fuzzy classifier to Low-order TSK fuzzy classifier, which resulting in Low-order TSK fuzzy classifier endowed with enhanced performance surpassing or at least comparable to High-order TSK classifier, as well as high interpretability; specifically 2) The Negative Euclidean distance between the output of teacher model and each class is employed to obtain the teacher logits, and then it compute teacher/student soft labels by the softmax function with distillating temperature parameter; 3) By reformulating the Kullback-Leibler divergence, it decouples fuzzy dark knowledge into target class knowledge and non-target class knowledge, and transfers them to student model. The advantages of HTSK-LLM-DKD are verified on the benchmarking UCI datasets and a real dataset Cleveland heart disease, in terms of classification performance and model interpretability

Classification of Hyperspectral and LiDAR Data Using Coupled CNNs

In this paper, we propose an efficient and effective framework to fuse hyperspectral and Light Detection And Ranging (LiDAR) data using two coupled convolutional neural networks (CNNs). One CNN is designed to learn spectral-spatial features from hyperspectral data, and the other one is used to capture the elevation information from LiDAR data. Both of them consist of three convolutional layers, and the last two convolutional layers are coupled together via a parameter sharing strategy. In the fusion phase, feature-level and decision-level fusion methods are simultaneously used to integrate these heterogeneous features sufficiently. For the feature-level fusion, three different fusion strategies are evaluated, including the concatenation strategy, the maximization strategy, and the summation strategy. For the decision-level fusion, a weighted summation strategy is adopted, where the weights are determined by the classification accuracy of each output. The proposed model is evaluated on an urban data set acquired over Houston, USA, and a rural one captured over Trento, Italy. On the Houston data, our model can achieve a new record overall accuracy of 96.03%. On the Trento data, it achieves an overall accuracy of 99.12%. These results sufficiently certify the effectiveness of our proposed model

Plant buffering against the high-light stress-induced accumulation of CsGA2ox8 transcripts via alternative splicing to finely tune gibberellin levels and maintain hypocotyl elongation

Ajuts: this study was supported by The National Key Research and Development Program of China (2019YFD1000300), the International Postdoctoral Exchange Fellowship Program from the China Postdoctoral Council (20170053), the Technology System Construction of Modern Agricultural Industry of Shanghai (19Z113040008), and the Presidential Foundation of Guangdong Academy of Agricultural Sciences (BZ201901).In plants, alternative splicing (AS) is markedly induced in response to environmental stresses, but it is unclear why plants generate multiple transcripts under stress conditions. In this study, RNA-seq was performed to identify AS events in cucumber seedlings grown under different light intensities. We identified a novel transcript of the gibberellin (GA)-deactivating enzyme Gibberellin 2-beta-dioxygenase 8 (CsGA2ox8). Compared with canonical CsGA2ox8.1, the CsGA2ox8.2 isoform presented intron retention between the second and third exons. Functional analysis proved that the transcript of CsGA2ox8.1 but not CsGA2ox8.2 played a role in the deactivation of bioactive GAs. Moreover, expression analysis demonstrated that both transcripts were upregulated by increased light intensity, but the expression level of CsGA2ox8.1 increased slowly when the light intensity was >400 µmol·m −2 ·s −1 PPFD (photosynthetic photon flux density), while the CsGA2ox8.2 transcript levels increased rapidly when the light intensity was >200 µmol·m −2 ·s −1 PPFD. Our findings provide evidence that plants might finely tune their GA levels by buffering against the normal transcripts of CsGA2ox8 through AS

An Object-Oriented Color Visualization Method with Controllable Separation for Hyperspectral Imagery

Publisher's version (útgefin grein)Most of the available hyperspectral image (HSI) visualization methods can be considered as data-oriented approaches. These approaches are based on global data, so it is difficult to optimize display of a specific object. Compared to data-oriented approaches, object-oriented visualization approaches show more pertinence and would be more practical. In this paper, an object-oriented hyperspectral color visualization approach with controllable separation is proposed. Using supervised information, the proposed method based on manifold dimensionality reduction methods can simultaneously display global data information, interclass information, and in-class information, and the balance between the above information can be adjusted by the separation factor. Output images are visualized after considering the results of dimensionality reduction and separability. Five kinds of manifold algorithms and four HSI data were used to verify the feasibility of the proposed approach. Experiments showed that the visualization results by this approach could make full use of supervised information. In subjective evaluations, t-distributed stochastic neighbor embedding (T-SNE), Laplacian eigenmaps (LE), and isometric feature mapping (ISOMAP) demonstrated a sharper detailed pixel display effect within individual classes in the output images. In addition, T-SNE and LE showed clarity of information (optimum index factor, OIF), good correlation (ρ), and improved pixel separability () in objective evaluation results. For Indian Pines data, T-SNE achieved the best results in regard to both OIF and, which were 0.4608 and 23.83, respectively. However, compared with other methods, the average computing time of this method was also the longest (1521.48 s).This research was funded by the National Natural Science Foundation of China, grant numbers 61275010 and 61675051. The authors would like to thank D. Landgrebe from Purdue University for providing the AVIRIS Indian Pines data set and Prof. P. Gamba from the University of Pavia for providing the ROSIS-3 University of Pavia data set. The authors would like to express their appreciation to Jon Qiaosen Chen from the University of Iceland and Di Chen for helping improve the language of the paper.Peer Reviewe