Representation Learning for Texts and Graphs: A Unified Perspective on Efficiency, Multimodality, and Adaptability

[...] This thesis is situated between natural language processing and graph representation learning and investigates selected connections. First, we introduce matrix embeddings as an efficient text representation sensitive to word order. [...] Experiments with ten linguistic probing tasks, 11 supervised, and five unsupervised downstream tasks reveal that vector and matrix embeddings have complementary strengths and that a jointly trained hybrid model outperforms both. Second, a popular pretrained language model, BERT, is distilled into matrix embeddings. [...] The results on the GLUE benchmark show that these models are competitive with other recent contextualized language models while being more efficient in time and space. Third, we compare three model types for text classification: bag-of-words, sequence-, and graph-based models. Experiments on five datasets show that, surprisingly, a wide multilayer perceptron on top of a bag-of-words representation is competitive with recent graph-based approaches, questioning the necessity of graphs synthesized from the text. [...] Fourth, we investigate the connection between text and graph data in document-based recommender systems for citations and subject labels. Experiments on six datasets show that the title as side information improves the performance of autoencoder models. [...] We find that the meaning of item co-occurrence is crucial for the choice of input modalities and an appropriate model. Fifth, we introduce a generic framework for lifelong learning on evolving graphs in which new nodes, edges, and classes appear over time. [...] The results show that by reusing previous parameters in incremental training, it is possible to employ smaller history sizes with only a slight decrease in accuracy compared to training with complete history. Moreover, weighting the binary cross-entropy loss function is crucial to mitigate the problem of class imbalance when detecting newly emerging classes. [...

Leavers and Remainers after the Brexit referendum:More united than divided after all?

Since the British “Brexit referendum” in 2016, tensions between ‘leave’ and ‘remain’ voters have been growing. Using a novel analytical approach based on the full distribution of responses rather than their arithmetic means, Study 1 (N=1,506) showed on average 90% of overlap among Leavers and Remainers across a range of important variables. Even on the variables that are commonly used to illustrate how Leavers and Remainers differ (e.g., prejudice against immigrants), the amount of overlap was larger than 50%. In Study 2 (N=206), we demonstrate the importance of focusing on similarities between groups: Presenting the full distribution of responses from Leavers and Remainers rather than their mean differences caused more accurate perceptions of the actual differences and similarities between both groups and higher perceived intergroup harmony. We conclude that similarities between Leavers and Remainers are substantial and that our proposed approach may help to de-escalate tensions between these and other groups

Volumes of moduli spaces of hyperbolic surfaces with cone points

In this paper we study volumes of moduli spaces of hyperbolic surfaces with geodesic, cusp and cone boundary components. We compute the volumes in some new cases, in particular when there exists a large cone angle. This allows us to give geometric meaning to Mirzakhani's polynomials under substitution of imaginary valued boundary lengths, corresponding to hyperbolic cone angles, and to study the behaviour of the volume under the $2\pi$ limit of a cone angle.Comment: 32 page

Development of a low-cost method for quantifying microplastics in soils and compost using near-infrared spectroscopy

Near-infrared (NIR) spectroscopy is a promising candidate for low-cost, nondestructive, and high-throughput mass quantification of microplastics in environmental samples. Widespread application of the technique is currently hampered mainly by the low sensitivity of NIR spectroscopy compared to thermoanalytical approaches commonly used for this type of analysis. This study shows how the application of NIR spectroscopy for mass quantification of microplastics can be extended to smaller analyte levels by combining it with a simple and rapid microplastic enrichment protocol. For this purpose, the widely used flotation of microplastics in a NaCl solution, accelerated by centrifugation, was chosen which allowed to remove up to 99% of the matrix at recovery rates of 83%–104%. The spectroscopic measurements took place directly on the stainless-steel filters used to collect the extracted particles to reduce sample handling to a minimum. Partial least squares regression models were used to identify and quantify the extracted microplastics in the mass range of 1–10 mg. The simple and fast extraction procedure was systematically optimized to meet the requirements for the quantification of microplastics from common polyethylene-, polypropylene-, and polystyrene-based packaging materials with a particle size 10% determined by loss on ignition). Microplastics could be detected in model samples at a mass fraction of 1 mg g−1. The detectable microplastic mass fraction is about an order of magnitude lower compared to previous studies using NIR spectroscopy without additional enrichment. To emphasize the cost-effectiveness of the method, it was implemented using some of the cheapest and most compact NIR spectrometers available.BMBFPeer Reviewe

Determination of the Origin of Bull Trout below Cabinet Gorge Dam

Migratory bull trout (Salvelinus confluentus) that spawned in the Clark Fork River drainage historically inhabited Lake Pend Oreille as subadult and adult fish. If this premise is correct, the construction of Cabinet Gorge Dam disrupted the connectivity of the Pend Oreille / Clark Fork metapopulation. We used eight microsatellite loci to compare bull trout collected at the base of Cabinet Gorge Dam to fish sampled both above and below the dam. These data were analyzed using Cavalli-Sforza and Edwards\u27 (CSE) chord distance and Paetkau’s individual assignment test. The CSE UPGMA and Paetkau’s individual assignment test suggest that Cabinet Gorge bull trout are genetically more similar to tributaries sampled from above the dam. This relationship suggests that the risks associated with passing a limited number of radio tagged adults are minimal compared to the potential genetic and demographic benefits to populations located above the dam