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Life from bad smells
No full textFocus:
To understand that hydrogen sulphide indicates that there is life at the bottom of the deep-sea.
Learning objectives:
With this activity, we create a nasty smell similar to hydrogen sulphide from rotting organic matter. We use that smell as a foundation for a discussion/lesson on interesting life, animals and food webs from the deep sea floor.
Key words:
Deep-sea, hydrogen sulphide, chemosynthesis, nematodes, tube worms, food webs
Sculpting foraminifera
No full textFocus:
To appreciate the biodiversity of the Arctic Ocean and ocean floor, with a particular focus on lesser-known species like foraminifera.
Learning objectives:
In this activity, pupils will identify (one, two, or several) foraminifera species living at or close to the Arctic Ocean floor and sculpt them using modelling clay.
Key words:
Foraminifera, Benthic, Planktonic
Наскільки глибокий океан?
No full textFocus:
To gain an appreciation of ocean depth using ideas of scale and physical education.
Learning objectives:
With these TWO activities, pupils will begin to understand (and feel) how deep the ocean can be in relation to their own size and height. They will get a feeling of the difference between height (above ground) and depth (in relation to water).
Key words:
Height, (ocean) depth.Фокусуємося на:
визначенні глибини океану за допомогою масштабу та фізичної культури.
Цілі навчання:З цими ДВОМА діяльностями учні почнуть розуміти (і відчувати), наскільки глибоким може бути океан порівняно з їхньою власною висотою та зростом. Вони отримають відчуття різниці між висотою (над землею) та глибиною (відносно води).
Ключові слова:
Висота, (океанічна) глибин
Діорама океанського дна
No full textFocus:
To consolidate pupils\u27 learning about the ocean floor in a creative and fun way. Through creating a diorama, the pupils will think about what the ocean floor looks like and what species live there.
Learning objectives:
In this activity, pupils will design, classify, and find a place for all the elements and characters that compose the ocean floor and biodiversity that they have learnt about in class.Фокусуємося на:
закріпленні знань про дно океану у творчій та веселій формі. Створюючи діораму, учні будуть використовувати знання про те, як виглядає дно океану і які види там живуть.
Мета навчання:
У цій вправі учні сконструюють, класифікують і знайдуть місце для всіх істот і персонажів, які складають дно океану та біорізноманіття, про які вони дізналися на уроці
How deep is the ocean?
No full textFocus:
To gain an appreciation of ocean depth using ideas of scale and physical education.
Learning objectives:
With these TWO activities, pupils will begin to understand (and feel) how deep the ocean can be in relation to their own size and height. They will get a feeling of the difference between height (above ground) and depth (in relation to water).
Key words:
Height, (ocean) depth
Biographical Introduction, Summary of Contents and Appendix with bibliography of references
No full textThe fourteenth volume in the series presents a dissertation by Conradus (Conrad) Quensel (1676–1732), professor at Lund University in Sweden, and his student, Johannes (Hans) Eurodius (1703–1756). In the text, detailed descriptions of two northern lights seen in Lund in the autumn of 1726 are accompanied by ample discussion of observations made ten years earlier in both Sweden and on the Continent, where an exceptionally strong auroral outbreak was seen in March 1716. The theoretical deliberations of Quensel/Eurodius largely follow theories presented by Friedrich Wolff and Johann Friedrich Weidler in the aftermath of the 1716 event. As praeses, Quensel was responsible for the contents, whereas Eurodius acted as respondens, meaning that it was his task to defend the dissertation orally in a public defence. It is unclear who actually wrote the dissertation text. The introduction, written by neo-Latinist and historian of science Per Pippin Aspaas, contains biographical information about the authors as well as a summary with ample extracts of the Latin text in English translation. A list explaining the references in Quensel/Eurodius’ text rounds off the introduction
Introduction: The Swedish Societas Literaria et Scientiarum and the second decade of its Acta (1730–1739); Summary of contents pertaining to the aurora borealis
No full textThe sixteenth volume in the series presents all articles on the aurora borealis that were published in the journal of the Swedish Societas Regia Literaria et Scientiarum (now Kungl. Vetenskaps-Societeten i Uppsala) from 1730 to 1739. The articles are by the society’s secretary, the professor of astronomy in Uppsala, Anders (in Latin: Andreas) Celsius and by several other Swedish professionals and amateurs of science. In the introduction to this volume, neo-Latinist and historian of science Per Pippin Aspaas summarizes the contents of all articles dealing with the aurora and presents extracts of these texts in English translation. He also provides a short history of the society in the period and gives brief presentations of Anders Celsius, Herman Spöring, Sven Hof, Johan Göstaf Hallman, Johan Sparschuch and Nils Wallerius as auroral researchers
Using Mask R-CNN for Underwater Fish Instance Segmentation as Novel Objects: A Proof of Concept
No full textInstance Segmentation in general deals with detecting, segmenting and classifying individual instances of objects in an image. Underwater instance segmentation methods often involve aquatic animals like fish as the things to be detected. In order to train deep learning models for instance segmentation in an underwater environment, rigorous human annotation in form of instance segmentation masks with labels is usually required, since the aquatic environment poses challenges due to dynamic background patterns and optical distortions.However, annotating instance segmentation masks on images is especially time- and cost-intensive compared to classification tasks.Here we show an unsupervised instance learning and segmentation approach that introduces a novel class, e.g., ""fish"" to a pre-trained Mask R-CNN model using its own detection and segmentation capabilities in underwater images.Our results demonstrate a robust detection and segmentation of underwater fish in aquaculture without the need for human annotations.This proof of concept shows that there is room for novel objects within trained instance segmentation models in the paradigm of supervised learning
Efficient Self-Supervision using Patch-based Contrastive Learning for Histopathology Image Segmentation
No full textLearning discriminative representations of unlabelled data is a challenging task. Contrastive self-supervised learning provides a framework to learn meaningful representations using learned notions of similarity measures from simple pretext tasks. In this work, we propose a simple and efficient framework for self-supervised image segmentation using contrastive learning on image patches, without using explicit pretext tasks or any further labeled fine-tuning. A fully convolutional neural network (FCNN) is trained in a self-supervised manner to discern features in the input images and obtain confidence maps which capture the network\u27s belief about the objects belonging to the same class. Positive- and negative- patches are sampled based on the average entropy in the confidence maps for contrastive learning. Convergence is assumed when the information separation between the positive patches is small, and the positive-negative pairs is large. The proposed model only consists of a simple FCNN with 10.8k parameters and requires about 5 minutes to converge on the high resolution microscopy datasets, which is orders of magnitude smaller than the relevant self-supervised methods to attain similar performance. We evaluate the proposed method for the task of segmenting nuclei from two histopathology datasets, and show comparable performance with relevant self-supervised and supervised methods
Automatic Consistency Checking of Table and Text in Financial Documents
No full textA company\u27s financial documents use tables along with text to organize the data containing key performance indicators (KPIs) (such as profit and loss) and a financial quantity linked to them. The KPI’s linked quantity in a table might not be equal to the similarly described KPI\u27s quantity in a text. Auditors take substantial time to manually audit these financial mistakes and this process is called consistency checking. As compared to existing work, this paper attempts to automate this task with the help of transformer-based models. Furthermore, for consistency checking it is essential for the table\u27s KPIs embeddings to encode the semantic knowledge of the KPIs and the structural knowledge of the table. Therefore, this paper proposes a pipeline that uses a tabular model to get the table\u27s KPIs embeddings. The pipeline takes input table and text KPIs, generates their embeddings, and then checks whether these KPIs are identical. The pipeline is evaluated on the financial documents in the German language and a comparative analysis of the cell embeddings\u27 quality from the three tabular models is also presented. From the evaluation results, the experiment that used the English-translated text and table KPIs and Tabbie model to generate table KPIs’ embeddings achieved an accuracy of 72.81% on the consistency checking task, outperforming the benchmark, and other tabular models