Miracle-dz/UniFAR-FTUs huggingface dataset:
3D Corporate Tourism in the Marine Sciences: Application-Oriented Problem Solving in Marine and Coastal Ecosystems

3D corporate tourism in the marine sciences is a solution-based approach to innovation in science, engineering and design. Corporate international scientists, engineers and designers work with local experts in Malaysian marine and coastal environments: they jointly discover, develop and design complex materials and designs inspired by nature directly on site (e.g. at the UKM Marine Ecosystem Research Centre EKOMAR and Malaysian Marine Parks) and construct initial biomimetic prototypes and novel designs. Thereby, new links, networks and collaborations are established between communities of thinkers in different countries. 3D tourism aims at mapping new frontiers in emerging engineering and design fields. This provides a novel way to foster and promote innovative thinking in the sciences, and considers the need for synergy and collaboration between marine sciences, engineering and design rather than segmentation and isolation. With the concept of 3D corporate tourism the potential of Malaysian marine ecosystems is used in a sustainable way and the management of marine resources for human and environmental wellbeing is fostered, without exploiting the natural resources or removing anything else from the ecosystem apart from ideas. Subsequent deeper and more detailed investigations at the respective international home institution foster collaborations and result in synergistic effects across borders.
Selekcja klas kompatybilności z zastosowaniem teorii hipergrafów

The problem of memory size reduction is a very important part of design process of discrete systems. The prototyped memory size very often exceeds the size of memory blocks offered by programmable devices (in example FPGAs). One of the most popular solutions to this problem is memory size reduction. The reduction process is based on selection of the compatibility classes of microoperations. Three methods of selection of compatibility classes are presented in the paper. The first one is a well- known method of selection, to which the fast reduction algorithm is applied. The algorithm bases on the matrix operations, which can also be represented as reduction of the hypergraph incidence matrix. In each step some vertices and edges are reduced. The reduced matrix holds the final result. The two other solutions introduced in the paper are based on the idea of computation of the minimal transversal (vertices covering) of hypergraphs. Proper microoperations are represented by the hypergraph vertices, while compatibility classes are described by the hyperedges. Therefore, any method of hypergraph transversal calculation can be applied to achieve the selection. In the paper the authors propose and analyse the effectiveness of backtracking and greedy algorithms. The proposed solutions are compared with the traditional method, which is based on transformation of the hypergraph incidence matrix. The obtained results of experiments are analysed and discussed in detail.
Parasitoid Increase During an Outbreak of a Native Herbivorous Insect Following Small-scale Amur Honeysuckle (Lonicera maackii) Removal

Abstract. Recolonization by native species following removal of invasive plant species can often be uneven and lead to the rapid increase of one or a few native plant species. This can result in the formation of a significant resource pulse that may consequently affect populations of herbivorous species and their natural enemies. Here we present results from observations of parasitism rates during a localized outbreak of the Asimina webworm moth, Omphalocera munroei, a locally monophagous herbivore of the common paw-paw. Asimina triloba. This outbreak initiated from locations of increased understory growth of A. triloba, following the removal of Amur Honeysuckle (Lonicera maackii). Parasitism rates during the outbreak reached 50%, with higher parasitism rates observed in larvae collected at the end of the local outbreak relative to those the year following the peak of the outbreak. Parasitism rates remained high 3 y after the end of the local O. munroei outbreak, indicating >7 y of high parasitoid densities. O. munroei emerges late in the growing season, making it fairly inaccessible as a host or prey to many generalist predators/parasitoids, which emerge earlier the following year. This suggests the O. munroei outbreak potentially contributed to an increase in natural enemy pressure of other native species in the community.
Laboratory modelling of breaking internal solitary waves

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β-Hydroxybutyrate upregulates FGF21 expression through inhibition of histone deacetylases in hepatocytes

Abstract Fibroblast growth factor 21 (FGF21) is secreted by hepatocytes as a peptide hormone to regulate glucose and lipid metabolism. FGF21 promotes hepatic ketogenesis and increases ketone body utilization in starvation. Histones are the target molecules of nutrients in regulating hepatic metabolic homeostasis. However, the effect of ketone bodies on FGF21 expression and the involvement of histones in it is not clear yet. The present study observed the effects of β-hydroxybutyrate (β-OHB), the main physiological ketone body, on FGF21 expression in human hepatoma HepG2 cells in vitro and in mice in vivo, and the role of histone deacetylases (HDACs) in β-OHB-regulated FGF21 expression was investigated. The results showed that β-OHB significantly upregulated FGF21 gene expression and increased FGF21 protein levels while it inhibited HDACs’ activity in HepG2 cells. HDACs’ inhibition by entinostat upregulated FGF21 expression and eliminated β-OHB-stimulated FGF21 expression in HepG2 cells. Intraperitoneal injections of β-OHB in mice resulted in the elevation of serum β-OHB and the inhibition of hepatic HDACs’ activity. Meanwhile, hepatic FGF21 expression and serum FGF21 levels were significantly increased in β-OHB-treated mice compared with the control. It is suggested that β-OHB upregulates FGF21 expression through inhibition of HDACs’ activity in hepatocytes.
NCU-IISR/AS-GIS: Using BERTScore and Snippet Score to Improve the Performance of Pretrained Language Model in BioASQ 10b Phase B

This paper presents our system for the BioASQ10b Phase B task. For ideal answers, we used the fine-tuned BioBERT model on the MNLI dataset to construct sentence embeddings and combined it with BERTScore to select sentences from the provided Snippets as answers. For the exact answers, we also used the BioBERT model and used the snippet scores generated from the ideal answer selection model to predict the exact answers for factoid and list questions. The exact answers of our fifth test batch ranked second place. In addition, the ideal answers we submitted achieved first place in the ROUGE score in all test batches from batch second to fifth.
SCUoL at CheckThat!-2022: Fake News Detection Using Transformer-Based Models

The fifth edition of the "CheckThat! Lab" is one of the 2022 Conference and Labs of the Evaluation Forum (CLEF) and aims to evaluate advances supporting three factuality-related tasks, covering several languages. Our team (SCUoL) participated in task 3A, which concentrates on multi-class fake news detection of English news articles. This paper describes our approach, including several experiments exploring different machine learning and transformer-based models. Furthermore, we employed an additional dataset to support our proposed model. During the validation results phase, the experiments highlight the best performing machine learning classifier, which achieved cross-validation scores of over 60% for the LinearSVC compared to the pre-trained BERT model that exceeds other models in this task. While in the testing results, we obtained an F1 of approximately 0.305 compared to the other participants’ average F1 of 0.252.
LauSAn at eRisk 2022: Simply and Effectively Optimizing Text Classification for Early Detection

The goal of early detection tasks at eRisk is to classify social media users as early as possible, based on streams of posts written by those users. We present two simple strategies of adapting standard text classification models in order to optimize them for early detection: concatenating the posts in different ways during training and inference, and continuously moving the decision boundary at inference time. We applied these approaches to two different text classification architectures based on pre-trained language models in eRisk 2022’s Task 2 (early detection of depression), and were able to reach top 5 placements in all time-sensitive evaluation metrics. A systematic post-submission ablation study confirmed that both strategies were effective at optimizing for early detection.
Overview of SnakeCLEF 2022: Automated Snake Species Identification on a Global Scale

The main goal of the third year of the SnakeCLEF challenge was to provide an evaluation platform that helps track the performance of AI-driven methods for snake species recognition systems on a global scale and allows direct comparison with human experts. We ran two challenges separately for humans — experts and novices — and AI methods in order to lay the groundwork for future comparison between human and machine-based snake species identification. We have provided 187,129 snake observations with 318,532 photographs — 270,251 for training and 48,281 for testing — of 1,572 snake species collected in 208 countries. The human performance evaluation was conducted on a tailored subset with 150 images derived from the full test set. We report (i) a description of the provided data, (ii) evaluation methodology and principles, (iii) an overview of the methods submitted by the participating teams, and (iv) a discussion of the obtained results.
Exploring Transformers for Multilingual Historical Named Entity Recognition

This paper explores the performance of out-of-the-box transformers language models for historical Named Entity Recognition (NER). Within the HIPE2022 (Identifying Hi storical P eople, P laces, and other E ntities) shared task, we participated in the NER-COARSE task of the Multilingual Newspaper Challenge (MNC). Three main approaches are experimented with: ensembling techniques on multiple fine-tuned models, using multilingual pretrained models, and relabeling the entity tags from the IOB-segmentation to a simplified version. By ensembling predictions from different system outputs, we outperformed the baseline model in the majority of cases. Moreover, through post-submission experiments, we found that using multilingual models did not yield better results compared to monolingual models. Furthermore, the relabeling experiment on the Newseye French dataset showed that merging entity labels and inferring the IOB segmentation in postprocessing increases precision but lowers recall. Last but not least, soft-label ensembling experiments on the same dataset enhanced precision, recall and thus F1-scores compared to hard-label ensembling by at least one percentage point.
PoliMi-FlatEarthers at CheckThat!-2022: GPT-3 applied to claim detection

A crucial task when fighting online misinformation is the automatic filtering of potential claims among the millions of posts and texts uploaded every day to social media. Most approaches to this problem have focused on the training and fine-tuning of BERT-related models [1][2]. We show how much larger GPT-3 [3] models, despite being developed primarily for text-generation, outperformed previous language models on the task of automated claim detection on the 2022 CheckThat! Challenge [4] dataset. Not only that, we will also show that GPT-3, while designed for handling mainly English tasks, can maintain competitive performances on other languages as well.
Coeffective-Dolbeault cohomology of compact indefinite Kähler manifolds

In this paper we consider indefinite Kahler manifolds, that is, complex manifolds with a compatible indefinite metric such that the associated Kahler form is closed. The class of indefinite Kahler manifolds is a particular class of symplectic manifolds containing the class of Kahler manifolds. There exists some similarities of the theory of indefinite Kahler manifolds with the theory of Kahler manifolds, in particular, the formalism associated with the covariant derivative and the curvature operator [1, 3], but there exists also some known differences: 1. The minimal model of a compact Kahler manifold is formal [12], but there are examples of non-formal compact indefinite Kahler manifolds [6, 13, 14]. 2. Any compact Kahler manifold satisfies the Hard Lefschetz theorem [17], but this is in general false for compact indefinite Kahler manifolds [5]. 3. The Frόlicher spectral sequence of a compact Kahler manifold always collapses at the EI term [17], but examples of indefinite Kahler manifolds which Frόlicher spectral sequence may not collapse even at E<2 are known [7, 8, 9]. Notice that the known examples of indefinite Kahler manifolds with no Kahler structure or not satisfying Kahler properties are compact nilmanifolds or solvmanifolds. These classes of compact homogeneous manifolds have proved to be very useful in producing a rich and wide variety of examples of compact manifolds with special properties (see [2, 6, 11, 16]). In [4] T. Bouche defines a differential subcomplex of de Rham complex on a symplectic manifold, and he obtains some results on the cohomology of this complex: the coeffective cohomology. In particular, he proves that the coeίfective cohomology is related to the de Rham cohomology for compact Kahler manifolds, but this is not true in general for any compact symplectic manifold (see [2, 15]). The aim of this paper is to introduce for indefinite Kahler manifolds a differential subcomplex of Dolbeault complex defined analogously to the above subcomplex for symplectic manifolds. More precisely, in Section 2, for an indefinite Kahler manifold M with Kahler form ω, we study the complex
A Type Discipline for Message Passing Parallel Programs

We present ParTypes, a type discipline for parallel programs. The model we have in mind comprises a fixed number of processes running in parallel and communicating via collective operations or point-to-point synchronous message exchanges. A type describes a protocol to be followed by each processes in a given program. We present the type theory, a core imperative programming language and its operational semantics, and prove that type checking is decidable (up to decidability of semantic entailment) and that well-typed programs do not deadlock and always terminate. The article is accompanied by a large number of examples drawn from the literature on parallel programming.
iCompass at CheckThat!-2022: ARBERT and AraBERT for Arabic Checkworthy Tweet Identification

This paper provides a detailed overview of systems and its achieved results, which were produced as part of CLEF2022 - Check-That! Lab Fighting the COVID-19 Infodemic and Fake News Detection. The task was carried out using transformers pre-trained models Arabic BERT, ARBERT, MARBERT, AraBERT, Arabic ALBERT and BERT base arabic. The models were fine-tuned for the down-stream task in hand, binary classification of Arabic tweets. According to the results, AraBERT attained the highest 0.462 F1 score on the test set of subtask 1A and ARBERT attained the best F1 score 0.557 on the test set of subtask 1C.
University of Amsterdam at the CLEF 2022 SimpleText Track

This paper reports on the University of Amsterdam’s participation in the CLEF 2022 SimpleText track. The overall goal of removing barriers that prevent the general public from accessing scientific literature is of great importance to help users make sense of a world of misinformation and shallow opinions. We perform preliminary studies within the track’s setup, analyzing the text complexity of searching a large set of academic abstracts in the context of popular science topics emerging in the news, with a specific focus at the relation between the topical relevance and the text complexity of the retrieved information. Our main findings are the following. First, we analyzed a large corpus of scientific abstracts and confirmed that these are highly complex on average, but that the variation is large and many abstracts with accessible readability levels exist. Second, we ran retrieval experiments and found that standard search ignores readability, yet filtering on the desirable reading level still retains competitive performance while avoiding retrieving relevant but incomprehensible results. Third, we ran complexity spotting experiments and found that straightforward lexical complexity or term frequency measures are strong indicators, but have to be combined with the importance of the concept in the broader context of the information request. Fourth, we ran a GPT-2 based text simplification model in a zero-shot way, resulting in conservative rewriting of abstracts, able to significantly reduce the text complexity. More generally, our results demonstrate that text complexity is an essential aspect to consider for improving non-expert access to scientific information, and opens up new routes to develop effective scientific information access technology tailored to needs of the general public.
Stacked Model based Argument Extraction and Stance Detection using Embedded LSTM model

In this paper, we present our submission approach for the third Touché lab at CLEF 2022 [1], shared task 2 on Argument Retrieval for Comparative Questions, which tackles answering comparative questions based on argument retrieval of text passages to support answering comparative questions in the scenario of personal decision making. The previous two Touché editions [2] [3] mostly focused on retrieving complete arguments and documents, while this edition is about whether argument retrieval can support decision-making directly by extracting the argumentative gist from documents, by classifying their stance with respect to the objects compared. In our approach, we performed tokenization and named entity recognition using a RoBERTa classifier. Followed by generating Boolean queries by categorizing the words in must and should terms. The top 100 results are retrieved from an Elasticsearch index that contains the corpus provided for the shared task. Result documents are then stripped of code, advertisements, and other noise. A Stacked model with SVM model, DistilBERT, and a learning meta-model binary classification [4] is performed on a sentence level. Extracted arguments are then scored based on a mix of BM25, the ratio of argument sentences in a document, and the similarity of the query and the sentences in the document. Finally, stance detection is then performed on a per-document-base using a word-embedding LSTM model. Our system, achieved a retrieval performance quality score of 0.492 mean nDCG@5 and relevance score of 0.582 mean nDCG@5 which is a slight improvement over the baseline scores.
UNED-MED at eRisk 2022: depression detection with TF-IDF, linguistic features and Embeddings

Mental health problems, such as depression and anxiety, are conditions that can have very serious consequences if untreated, and cause the patient a lot of suffering. Research suggests that the way people write can reflect mental well-being and mental health risks, and social media provides a source of user-generated text to study. Early detection is crucial for mental health problems, and with this in mind the shared task eRisk was created. This paper describes the participation of the group UNED-MED on the 2022 T2 subtask. Participants were asked to create systems to detect early signs of depression on users from Reddit. Our team proposes two approaches: feature-driven classifiers with features based on text data, TF-IDF terms, first-person pronoun use, sentiment analysis and depression terminology; and a Deep Learning classifier with pretrained Embeddings. The official task results show modest results that show the difficulty of working with depression data.
Zero-shot Hybrid Retrieval and Reranking Models for Biomedical Literature

We describe our participating system in the document retrieval sub-task (Task B Phase A) at the 10th BioASQ challenge. We designed and implemented a zero-shot hybrid model using only synthetic training data. The model consists of two stages: retrieval and reranking. The retrieval model is a hybrid of sparse and dense retrieval models, which is an extension of our participating system at 8th BioASQ challenge. We improved the dense retrieval model with a T5-based synthetic question generation model and an iterative training strategy involving techniques to filter low-quality synthetic data. In the second stage, we proposed a hybrid reranking model, which is trained using the candidates retrieved from the first stage. We further explored whether the knowledge from the hybrid reranking model can be transferred to the dense retrieval model through distillation. Our experiments show the proposed hybrid ranking model is effective even when applied to different first-stage retrieval models. Furthermore, we explored the combination of different systems via reciprocal rank fusion and achieved additional accuracy gains. Evaluation shows that our model compares favorably with the top participating system, achieving MAP scores of 0.4696, 0.3984, 0.4586, 0.4089, 0.4065 and 0.1704 on six batches.
Dowsing for Answers to Math Questions: Doing Better with Less

We present our application of a new math-aware search engine to the 2022 ARQMath Lab. This extends the annual submissions from the University of Waterloo’s “MathDowsers,” for which we twice produced the best Task 1 participant run. This year the major improvements result from greatly reducing the number of math tuples used to represent a math formula, which not only saves considerable space in the index and reduces query terms, but also improves retrieval effectiveness as measured by normalized discounted cumulative gain with unjudged documents removed (nDCG’). In addition, we have re-implemented math tuple generation in a stand alone code base and replaced Tangent-L, the previous Lucene-based search engine, with a new engine that simplifies experimentation. Experimental results show that the new system has substantially better performance than its pre-decessor in terms of nDCG’, MAP’, and P’@10, both when trying to find answers to math questions (Task 1) and when trying to find similar formulas (Task 2). From this we conclude that traditional text retrieval systems can easily be enhanced to become effective math-aware search engines using the tools we have developed.
How good can an automatic translation of Pokémon names be?

For those of you who are not familiar with the successful franchise of Pokémon, it revolves around imaginary creatures often representing animals mixed with objects, plants, etc. Their names reflect these characteristics and are, most of the time, wordplays. For this paper some Pokémon names were automatically translated by using the translation model T5 with the use of Python. The aim of this paper is to evaluate the overall quality of such translation. The results were very diverse, most source sentences stayed unchanged but some others were surprising and interesting to point out for diverse reasons.