Miracle-dz/UniFAR-FTUs huggingface dataset:
Suppression of flow reversals via manipulating corner rolls in plane Rayleigh–Bénard convection

Abstract In this paper, we report that reversals of the large-scale circulation in two-dimensional Rayleigh–Bénard (RB) convection can be suppressed by imposing sinusoidally distributed heating to the bottom plate. We examine how the frequency of flow reversals depends on the dimensionless wavenumber $k$ of the spatial temperature modulation with various modulation amplitude $A$. For sufficiently large $k$, the flow reversal frequency is close to that in the standard RB convection under uniform heating. However, when $k$ decreases, the frequency of flow reversal gradually becomes lower and can even be largely reduced. Furthermore, we examine the growth of the corner roll and the global flow structure based on Fourier mode decomposition, and reveal that the size of the corner roll diminishes as the wavenumber decreases. The reason is that the regions occupied by the cold phase can absorb heat from the hot plumes and thus lower their temperature, which reduces the corner roll's kinetic energy input provided by the buoyancy force, and weakens the feeding process of the corner rolls. This results in the locking of the corner roll into a smaller region near the corner, making it harder for a reversal to occur. Using the concept of horizontal convection caused by non-uniform heating, we find a relevant parameter $k/A$ to describe briefly how the reversal frequency depends on wavenumber and modulation amplitude. The present work provides a new way to control the flow reversals in RB convection through modifying temperature boundary conditions.
An Assessment of Prairie Management Practices for Maintaining Habitat Quality for the Endangered Poweshiek Skipperling Butterfly in Canada

Abstract. The Poweshiek skipperling (Oarisma poweshiek) was once a common prairie butterfly in central North America, but is now critically endangered in Canada and the United States. The Poweshiek skipperling is confined to the largest remaining tall grass prairie in Canada, which is currently managed using grazing and fire to maintain prairie habitat and prevent forest and shrub encroachment. To support re-introduction, restocking, and recovery of this critically endangered species, it is necessary to understand the habitat conditions preferred by skipperlings. By surveying prairie sites with Poweshiek skipperling across age and treatment categories (1–2 y since burn, 4–6 y since burn, >15 y since burn, and grazing), we identified commonly-used nectar plants and recorded physical variables known to influence plant diversity. We measured soil variables, including macronutrients, compaction, pH, texture, and moisture content, and used multivariate statistics to test for significant differences in site characteristics and plant community across treatments. For each site, we captured plant diversity, abundance, and cover, as well as total plant biomass. We found the oldest burn sites had the fewest skipperlings. Intermediate burn sites and lightly grazed sites contained the most skipperlings. In 95% of nectaring observations, adult skipperlings were feeding on Black-eyed Susan (Rudbeckia hirta), upland white aster (Solidago ptarmicoides), and Self-heal (Prunella vulgaris). Characteristics in the physical components of sites and plant communities did not significantly differ across treatment types for most variables with the exception of the oldest burns, which showed significantly higher levels of soil compaction, live biomass, bare ground, less flowering species during the flight period, less flowering stems, and increased presence of nonnative and invasive species. Poweshiek skipperling is at high risk of imminent extirpation, and we recommend that the management regimes be adjusted to plan for a consistent supply of habitat conditions and plant species composition exemplified by our intermediate burn sites. We suggest fire return intervals of 4–6 y using patch burns in combination with grazing, permitted at times that minimize the impact on immature stages of Poweshiek skipperling. This management is needed to provide suitable habitat conditions to maintain the current population and allow for successful restocking and recovery.
Population and Community Responses of Small Mammals to Single-tree Selection Harvest in Laurentian Hardwood Forests

Abstract. Small mammals are ubiquitous members of vertebrate communities that are sensitive to habitat change. In the Great Lakes region of North America, small mammal communities have changed rapidly, but experimental tests of potential mechanisms are lacking. Using a before-after, control-treatment design, we quantified the response of small mammals to single-tree selection harvest in Laurentian hardwood forests of Wisconsin, United States. We documented changes in forest structure and small mammal abundance, species diversity, and community similarity from silvicultural treatment. Treatment reduced tree density and canopy cover and increased mean tree diameter, woody stem density, variation in woody stem density, and volume of coarse woody debris. Peromyscus and northern short-tailed shrews (Blarina brevicauda) were dominant community members across treatments and years. White-footed mice (P. leucopus) outnumbered woodland deer mice (P. maniculatus gracilis) before treatment, but declined by almost fifty percent after treatment; deer mice and total rodent (i.e., Rodentia) abundances were unchanged. Small mammal species diversity increased twofold following treatment. Our experiment identified species-specific responses within Peromyscus to timber harvest: white-footed mice, the numerically dominant and generalist species, were most sensitive to habitat change, and their response produced cascading effects to small mammal community structure. Future experiments should assess these small mammal responses in a multi-year framework and quantify their effects on the broader vertebrate community.
The entire chloroplast genome sequence of Asparagus cochinchinensis and genetic comparison to Asparagus species

Abstract Asparagus cochinchinensis is a traditional Chinese medicinal plant. The chloroplast (cp) genome study on A. cochinchinensis is poorly understood. In this research, we collected the data from the cp genome assembly and gene annotation of A. cochinchinensis, followed by further comparative analysis with six species in the genus Asparagus. The cp genome of A. cochinchinensis showed a circular quadripartite structure in the size of 157,095 bp, comprising a large single-copy (LSC), a small single-copy (SSC), and two inverted repeat (IR) regions. A total of 137 genes were annotated, consisting of 86 protein-coding genes, 8 ribosomal RNAs, 38 transfer RNAs, and 5 pseudo-genes. Forty scattered repetitive sequences and 247 simple sequence repeats loci were marked out. In addition, A/T-ending codons were shown to have a basis in the codon analysis. A cp genome comparative analysis revealed that a similar gene composition was detected in the IR and LSC/SSC regions with Asparagus species. Based on the complete cp genome sequence in Asparagaceae, the result showed that A. cochinchinensis was closely related to A. racemosus by phylogenetic analysis. Therefore, our study providing A. cochinchinensis genomic resources could effectively contribute to the phylogenetic analysis and molecular identification of the genus Asparagus.
A contrastive ecological discourse analysis of the General Debate statements by the US and China at the United Nations General Assembly (2017–2020)

Abstract This article adopts an ecolinguistic approach to study the thematic choices in the General Debate statements by the US and China at the United Nations General Assembly (2017–2020) to reveal the eco-characteristics of the two countries’ thematic choices and their impacts on the constructions of international relations. Findings suggest that, from the perspective of ecosophy, a significant proportion of the US’s thematic choices are destructive to international relations, while China’s thematic choices are mostly beneficial and ambivalent. The US’s thematic choices are frequently used to portray “us versus them” and “America first”, which displays a message of egocentrism instead of ecocentrism. China’s thematic choices are typically used to show China’s willingness to undertake global responsibility and to jointly work with other stakeholders to safeguard our planet, echoing China’s global vision of “building a shared future for mankind”.
COURAGE at CheckThat!-2022: Harmful Tweet Detection using Graph Neural Networks and ELECTRA

In this paper we propose a deep learning model based on graph machine learning (i.e. Graph Attention Convolution) and a pretrained transformer language model (i.e. ELECTRA). Our model was developed to detect harmful tweets about COVID-19 and was used to tackle subtask 1C (harmful tweet detection) at the CheckThat! Lab shared task organized as part of CLEF 2022. In this binary classification task, our proposed model reaches a binary F1 score (positive class label, i.e. harmful tweet) of 0.28 on the test set. We demonstrate that our approach outperforms the official baseline by 8% and describe our model as well as the experimental setup and results in detail. We also refer to limitations of the approach and future research directions. © 2022 Copyright (c) 2022 for this paper by its authors.
Overview of PlantCLEF 2022: Image-based plant identification at global scale

It is estimated that there are more than 300,000 species of vascular plants in the world. Increasing our knowledge of these species is of paramount importance for the development of human civilization (agri-culture, construction, pharmacopoeia, etc.), especially in the context of the biodiversity crisis. However, the burden of systematic plant identification by human experts strongly penalizes the aggregation of new data and knowledge. Since then, automatic identification has made considerable progress in recent years as highlighted during all previous editions of PlantCLEF. Deep learning techniques now seem mature enough to address the ultimate but realistic problem of global identification of plant biodiversity in spite of many problems that the data may present (a huge number of classes, very strongly unbalanced classes, partially erroneous identifications, duplications, variable visual quality, diversity of visual contents such as photos or herbarium sheets, etc). The PlantCLEF2022 challenge edition proposes to take a step in this direction by tackling a multi-image (and metadata) classification problem with a very large number of classes (80k plant species). This paper presents the resources and evaluations of the challenge, summarizes the approaches and systems employed by the participating research groups, and provides an analysis of key findings.
Lexicon-Based Profiling of Irony and Stereotype Spreaders

The PAN 22 Author Profiling Shared Task (IROSTEREO) aims to profile authors spreading irony and stereotypes on Twitter. In this paper, we report that from our experiments involving different classification methods – traditional n-gram approach, state-of-the-art language models, and lexical approach using LIWC, the best result was obtained from the lexicon-based approach (LIWC) with the accuracy score of 0.88 on the validation data and 0.92 on the official test data. Furthermore, we perform ablation experiments to identify some of the most informative features for irony and stereotype profiling
FAST-MT Participation for the JOKER CLEF-2022 Automatic Pun and Humour Translation Tasks

This paper presents the solution proposed by team FAST Machine Translation to the shared tasks of JOKER CLEF 2022 Automatic pun and humour translation. State-of-the-art Transformer-based models are used to solve the three tasks introduced in the JOKER CLEF workshop. The Transformer model is a kind of neural network that tries to learn the contextual information from the sequential data by implicitly comprehending the existing relationships. In task 1, given a piece of text, we need to classify/explain any instance of wordplay is present in it or not. The proposed solution to task 1 combines the pipeline of token classification, text classification, and text generation. In task 2, we need to translate single words (nouns) containing a wordplay. This task is mapped to the problem of question answering (Q/A) on programmatically extracted texts from the OPUS parallel corpus. In task 3, contestants are required to translate the entire phrase containing the wordplay. Sequence-to sequence translation models are used to solve this task. The team has adopted different strategies for each task as they suited to the requirements therein. The paper reports proposed solutions, implementation details, experimental studies, and results obtained in JOKER CLEF 2022 automatic pun and humour translation tasks.
NeuralDynamicsLab at ImageCLEFmedical 2022

Diagnostic Captioning is described as the automatic text generation from a collection of X-RAY images and it can assist inexperienced doctors and radiologists to reduce clinical errors or help experienced professionals to increase their productivity. Therefore, tools that would help doctors and radiologists produce higher quality reports in less time could be of high interest for medical imaging departments, as well as significantly impact deep learning research within the biomedical domain. With our participation in ImageCLEFmedical 2022 Caption evaluation campaign, we have attempted to address both concept detection and caption prediction tasks by developing baselines based on Deep Neural Networks; including image encoders, classifiers and text generators. Our group, NeuralDynamicsLab at KTH Royal Institute of Technology, within the school of Electrical Engineering and Computer Science, ranked 4 th in the former and 5 th in the latter task.
BERT-based ironic authors profiling

The present paper addresses the PAN at CLEF 2022 challenge ”Profiling Irony and Stereotype Spreaders on Twitter“ (IROSTEREO). The challenge strives to identify whether an author spreads sarcasm through their tweets. In general, author profiling tasks, whether mechanical or manual, are based on extensive author text. Many machine learning-based author profiling studies indicate that author-by-author classification could benefit the performance compared with a text-by-text way. We address the challenge through fine-tuning BERT model. BERT has shown satisfactory results on many natural language processing tasks. However, BERT model cannot exert its advantages in some specific tasks, like handling long documents, due to its limitation on the maximum input token length. Our author profiling task is one of these specific tasks. The present work addresses this dilemma through a re-segmentation approach: We first concatenate all tweets of an author into one document representation. We then split the document in such a way that the split text lengths do not exceed the maximum input token length of the BERT model and that we still retain the advantages of continuous text through an appropriate choice of overlap. Ultimately, the BERT model uses the hard voting method made the final decision. Our work first compares the performance of two pre-trained BERT models, i.e., the RoBERTa and BERTweet models, trained with external datasets. Then, we fine-tune BERT models with three different loss functions. In addition, we also demonstrate and evaluate a BERT feature-based CNN model. The winning models of the PAN author profiling task in recent years are re-implemented as baselines. Finally, the BERTweet model trained with the cross-entropy weighted focal loss function achieves an accuracy of 98.89% on the official test set. Adding a further soft voting ensemble method, which integrates BERTweet models with different loss functions as well as the BERT feature-based CNN model, we placed first in the challenge and improved our model performance to 99.44%. 1
Theoretical spectroscopy of realistic condensed matter systems

Le sujet de cette these porte sur des calculs ab initio de proprietes d'etat fondamental et excite de systemes differents, dans le cadre de la dft et de la tddft. Du cote numerique, nous avons mis en oeuvre une methode originale pour l'evaluation de la polarisabilite dynamique a particules independantes, et generalise un code au spin dans le but d'etudier les proprietes magnetiques de systemes reels. Nous avons etudie des spectres de reflectivite anisotrope et de perte d'energie pour la surface (100) du silicium, propre ou oxydee. La comparaison entre les spectres mesures et simules, nous ont permis d'exclure la reconstruction p(2x1). En suite, nous avons mis en evidence le probleme de la description des excitations des systemes a couche ouverte presente un etude des proprietes optiques d'alliage magnetique interessants pour des applications en spintronique. Nous avons evalue les proprietes d'etat fondamental de ces alliages et la conductivite du fer ccc.
LOCAL HUMAN SKIN POTENTIAL-SPATIAL DISTRIBUTION, MAGNITUDE AND ORIGIN

1 This thesis is an investigation of the sources and distribution of the slowlyvarying electric potential across hairy skin and the-spatial distribution of the eccrine gland pores, which may be related to the skin potential. The electrical characterization of skin may lead to non-invasive means of quantifying skin and skin diseases and monitoring the effectiveness and progress of dermatologic treatment as well as providing insight into fundamental skin physiology. The transdermal electrical potential in the same area on the back of the ·hands of eight subjects was measured under experimental conditions designed to minimize eccrine gland activity. The mean potential over all subjects was 24.86±8.16 mY. The pore distribution was evaluated by transferring the imprint of pilocarpinestimulated gland secretions on special paper to digitized arrays. The average gland count over all records for the mean electrode radius (1 mm) was 9.09±3.55 glands. The correlation between the measured potential and the number of glands at each measurement site was calculated, and in general, the measured potential was independent of gland count. Where some correlation was observed, it was probably due to experimental error. Care was taken to ensure that both distributions were adequately sampled. This lack of correlation was consistent with an equivalent circuit model for non-sudorific potential generation. This model predicted a minimal
Classification of Fungi Species: A Deep Learning Based Image Feature Extraction and Gradient Boosting Ensemble Approach

Deep learning has been successful in a variety of challenging image classification tasks, characterized by complex and large datasets. Ensemble learning further improves model performance by inferring compound decision boundaries in the feature space, and assorting importance to the most representative features to effectively discriminate between image classes. This paper details a deep learning based feature extraction and subsequent boosting ensemble approach for fungi species classification. The proposed workflow leverages state-of-the-art deep learning architectures such as ResNeXt and Efficient-Net among others, trains them by transfer learning onto a fungi image dataset for feature extraction, and finally integrates the output representation vectors with geographic metadata to train a gradient boosting ensemble classifier that predicts the fungi species. The authors trained multiple deep learning architectures, assessed their individual performance and selected effective feature extraction models. Multiple experiments were performed in choosing these models, and to subsequently perform hyper-parameter tuning to train the boosting classifier. The approach attained a maximum macro-averaged F1-Score of 48 . 96% on the test data. The corresponding validation F1-Score and Accuracy scores were 50 . 22% and 85 . 11% , respectively. Furthermore, the best model exhibited more confident predictions than its inferior counterparts, indicating improved inter-class discerning.
Monitoring Coral Reefs Using Faster R-CNN

Monitoring coral reefs is an important procedure to protect the persistence of many marine species. The imageCLEFcoral 2022 Challenge aims to identify and annotate corals on underwarter images. These images vary in terms of quality and are therefore of a high complexity. While our investigation, we focused on the data set and searched for ways to improve the image quality. To be specific, we minimized the impact of color casts, and erratic annotations by a color balancing strategy, as well as combining the prediction results of the trained deep learning architectures on preprocessed and original images. Object detection was handled by deep learning entirely. In particular, faster R-CNN with a ResNet+FPN backbone network was the architecture of the choice. The merging strategy is based by a Non-maximum Suppression (NMS) and reduces therefore overlapping predictions. Additionally, we analyzed the impact of the depth of the chosen backbone network. We have identified a connection between increasing network depth and increasing accuracy for underwater imaging. Overall, our best approach achieved a MAP 0 . 5 value of 0.396.
HUKB at ChEMU 2022 Task 1: Expression-Level Information Extraction

This paper describes our results for the three tasks at ChEMU 2022: Task 1a (named entity recognition), Task 1b (event extraction), and Task 1c (anaphora resolution). We adopted a hybrid approach using deep learning models and a small set of post-processing rules for these tasks. For Tasks 1b and 1c, we adopted a pipeline approach for relation extraction, which combined mention detection with relation classification. In addition, we proposed post-processing methods for Task 1c that considered the results of Task 1a. Our system obtained an exact match F-score of 0.9412 and a relaxed match F-score of 0.9572 for Task 1a, an exact match F-score of 0.8865 and a relaxed match F-score of 0.9027 for Task 1b, and an exact match F-score of 0.7232 and an F-score of 0.8053 for Task 1c for each test set (private score). Although our approaches tried to consider the document-level context and relationships between the tasks, limitations remained.
AIT_FHSTP at CheckThat!-2022: Cross-Lingual Fake News Detection with a Large Pre-Trained Transformer

The increase of fake news in today’s society, partially due to the accelerating digital transformation, is a major problem in today’s world. This year’s CheckThat! Lab 2022 challenge addresses this problem as a Natural Language Processing (NLP) task aiming to detect fake news in English and German texts. Within this paper, we present our methodology and results for both, the monolingual (English) and cross-lingual (German) tasks of the CheckThat! challenge in 2022. We applied the multilingual transformer model XLM-RoBERTa to solve these tasks by pre-training the models on additional datasets and fine-tuning them on the original data as well as its translations for the cross-lingual task. Our final model achieves a macro F1-score of 15,48% and scores the 22 𝑡ℎ rank in the benchmark. Regarding the second task, i.e., the cross-lingual German classification, our final model achieves an F1-score of 19.46% and reaches the 4 𝑡ℎ rank in the benchmark.