dataesr/scientific-paragraphs-categorization huggingface dataset:
Samples with missingness larger than 20% were omitted from further analysis. Furthermore, SNPs with more than two alleles, a minimum depth and minor allele count lower than three and a missingness higher than 10% were omitted from further analysis using "VCFtools"  (Danecek et al. , 2011). Finally, to account for the effects of linkage between SNPs in the datasets, we pruned the VCF file based on a pairwise correlation threshold of 0. 1 and a window size of 10,000 bases using the package "SNPRelate"  (Zheng et al. , 2012)  implemented in R (version 4. 0. 3).
| Admixture analysis and PCA To infer admixture between samples across the whole dataset, ancestry coefficients for K number of ancestral populations were calculated using the sNMF function  (Frichot et al. , 2014)  of the "LEA" package  (Frichot & François, 2015)  implemented in R. K varied from 2 to 10 while performing 10 replicate runs for each K. For each value of K, the best run was selected based on the cross-entropy criterion. Ancestral proportions (Q values) were then visualized for K = 2 up to the value of K with the lowest cross-entropy level + 1 to ensure that further subdivision is not meaningful using the R package "ggplot2"  (Wickham, 2017). In order to investigate and visualize the partitioning of genetic variation, an analysis of principal components (PCA) was performed using the snpgdsPCA function of the R package "SNPRelate"  (Zheng et al.
, 2012). To test for significant differences between geographical groups, a permutational ANOVA (permanova) was carried out using the first seven eigenvectors as predictor variables and employing 10,000 permutations using the pairwise. adonis2 function of the "pairwiseAdonis" package (https://github. com/pmart ineza rbizu/ pairw iseAd onis). A p-value was calculated for every pairwise comparison, and the Benjamini-Hochberg method  (Benjamini & Hochberg, 1995)  was applied to correct for multiple testing.
| PoMo phylogenetic tree In order to infer a population-level phylogenetic tree, a Polymorphisms-aware phylogenetic Model (PoMo) was constructed  (De Maio et al. , 2015). This approach is well-suited for tree estimation in scenarios of incomplete lineage sorting and accounts for ancestral variation while being computationally more efficient than other methods. Secondly, bcftools consensus  (Li, 2011)  was used to create a whole genome alignment in fasta format using the B. dorsalis reference genome (GenBank assembly accession: GCA_020283865. 1). The alignment was then converted to a "counts file" containing the allele counts per population for each site with the publicly available python library "cflib" (https://github. com/pomo-dev/cflib). Sites with missing data were subsequently omitted from the count's file.
, 2012)  applying the Expectation-Maximization (EM) algorithm for exploring the maximum value of the log-likelihood function  (Milligan, 2003; Zheng et al. , 2012). Two networks were created. The first network includes all edges with edge weight in proportion to the respective kinship value. A second network was constructed by extracting the backbone structure of the network, simplifying the network by retaining the most important edges, using the disparity filter algorithm by  Serrano et al. (2009)  implemented in the R package "backbone" (available at: https://CRAN. R-proje ct. org/packa ge=backbone). By imposing a significance level α = 0. 05 or 0. 10, the links that carry weights that are not different than under the null hypothesis can be pruned from the network. This method is preferred above a global weight threshold pruning method and reveals the multilayered and often complex nature of the network  (Jones & Manseau, 2022).
ACK N OWLED G EM ENTS The authors acknowledge the  Plant Protection Platform  ( 3P , IBISA) where DNA extractions were conducted and  David Nestel  for providing us with samples of B. zonata. Funding for this research was provided by the  EU-FF-IPM   H2020   FF-IPM  project (Grant ID:  818184 ), and the  National Natural Science Foundation of China  (Grant ID:  31972341 ). The computational resources ( Stevin Supercomputer Infrastructure ) and services used in this work were provided by the  VSC (Flemish Supercomputer Center) , funded by  Ghent University, FWO , and the  Flemish Governmentdepartment EWI.
Semi-supervised methods Semi-supervised learning combines both supervised and unsupervised learning methods. Typically, a small number of labeled and largely unlabeled data is used for training purposes. Sometimes they are also referred to as the hybrid method. If there is a vast number of unlabeled data combined with insufficient labeled data, it can affect the classification accuracy. It is also referred to as imbalanced training data. Similarly, the classification can become inefficient and accurate if there is no labeled data for a particular class. Semi-supervised methods include self-training, generative models, and graph-based methods. A semi-supervised algorithm based on tolerance roughest and ensemble learning is recommended for such kinds of problems  (Shi et al. 2011). The missing class is extracted by approximation from the dataset and used as the labeled sample.
The experiment also showed that the larger batch training sizes were also found to be more useful in the training procedure. Importantly, in addition to BERT training 16GB of Books Corpus and English Wikipedia data, RoBERTa uses 160 GB of text for pre-training. The additional data includes the CommonCrawl News dataset (63 million articles, 76 GB), Web text corpus (38 GB), and Stories from Common Crawl (31 GB). This, combined with a massive 1024 V100 Tesla GPU's running for a day, resulting in pre-training of RoBERTa. Consequently, RoBERTa outperforms both BERT and XLNet  (Yang et al. 2019 ) on GLUE benchmark results. RoBERTa has been proven effective on a variety of tasks such as detection of mental illnesses  (Murarka et al. 2020) , offensive language detection  (Tanase et al. 2020) , protest event detection  (Re et al.
2021 ) etc. XLNet  (Yang et al. 2019)  model is an extension of the Transformer-XL model  (Dai et al. 2019). It is pre-trained using an autoregressive method like OpenGPT  (Radford et al. 2018 ) and bi-directional context modeling of BERT by maximizing the anticipated likelihood over all permutations of the input sequence factorization order. OpenGPT Transformer learns using left-to-right text representation for natural language generation, while BERT uses a bidirectional transformer for natural language understanding. XLNet is a generalized autoregressive (AR) language modeling method that uses a permutation language modeling objective to combine the advantages of AR and autoencoding (AE) methods. The XLNet neural architecture is built to work effortlessly and harmoniously with the AR objective, including integrating Transformer-XL and carefully designing the two-stream attention mechanism.
BERT is an Autoencoding (AE) based model, while XLNet is an Auto-Regressive (AR) that uses permutation language modeling. The permutation operation during pre-training allows the context to include to kens from both left and right, making it a generalized order-aware autoregressive language model. The proposed XLNet architecture is pre-trained using nearly ten times more data than the original BERT. It is also trained with a batch size eight times larger for half as many optimization steps, thus making it four times more sequences in pretraining than BERT. XLNet achieves substantial improvement over previous pretraining objectives on various tasks. It is claimed that the XLnet outperforms BERT on 20 tasks, often by a large margin. The findings of a study show that XlNet achieves slightly better results for potentially harmful and protective suicide-related content on Twitter  (Metzler et al.
Event tracking is related to the development of some events over time. Event summarization outlines an event from the given data, while the event forecasts the next event within a current event sequence. These topics are part of the Topic Detection and Tracking (TDT) field. Nourbakhsh et al. (2017)  address natural and artificial disasters on social media. They identified events from local news sources that may become global breaking news within 24 h. They used Reuters News Tracer, a real-time news detection and verification engine. It uses a fixed sphere decoding (FSD) algorithm to detect breaking stories in real-time from Twitter. Each event is shown as a cluster of tweets engaging with that story. By considering different data features, they applied an SGD and SVM classifier that detects breaking disasters from postings of local authorities and local news outlets.
It consists of two phases which are crisis detection and crisis recognition. In addition, another model for embedding tweets is also introduced. The experiments are conducted on C6 and C36 datasets. According to the authors, these models surpass state-of-the-art performance for detection and recognition problems by up to 8. 2% and 25. 0%, respectively. Arachie et al. (2019)  proposed an unsupervised approach for detecting sub-events in major natural disasters. Firstly, noun-verb pairs and phrases are extracted from tweets as an important sub-event prospect. In the next stage, the semantic embedding of extracted noun-verb pairs and phrases is calculated and then ranked against a crisis-specific ontology called management of Crisis (MOAC). After filtering these obtained candidate sub-events, clusters are formed, and topranked clusters describe the highly important sub-events.
The experiments are conducted on Hurricane Harvey and the 2015 Nepal Earthquake datasets. According to the authors, the approach outperforms the current state-of-the-art subevent identification from social media data. Forests fire have become a global phenomenon due to rising droughts and increasing temperatures, often attributed to global warming and climate change. The work  (Kibanov et al. 2017 ) tests the usefulness of social media to support disaster management. However, the primary data for dealing with such incidents come from NASA satellite imagery. The authors use GPS-stamped tweets posted in 2014 from Sumatra Island, Indonesia, which experiences many haze events. As confirmed by analyzing the dataset, Twitter has proven to be a valuable resource during such events. Furthermore, the authors also announced the development of a tool for disaster management.
Huang et al. (2021)  focus on emergency events. They consider the various type of events under the term "emergency events". It includes infectious diseases, explosions, typhoons, hurricanes, earthquakes, floods], tsunamis, wildfires, and nuclear disasters. To respond in time, the model must automatically identify the attribute information 3W (What, When, and Where) of emergency events. Their proposed solution contains three phases, the classification phase, the extraction phase, and the clustering phase, and it is based on the Similarity-Based Emergency Event Detection (SBEED) framework. The experiment is done using the Weibo dataset. Different classification models such as KNN, Decision Trees, Naïve Bayes, Linear SVC (RBF), and Text-CNN are used in the classification phase. Secondly, time and location are extracted from the classification obtained.
Lastly, an unsupervised dynamical text clustering algorithm   Pais et al. present  an unsupervised approach to detecting extreme sentiments on social networks. Online wings of radical groups use social media to study human sentiments engaging with uncensored content to recruit them. They use people who show sympathy for their cause to further promote their radical and extreme ideology. The authors developed a prototype system composed of two components, i. e. , Extreme Sentiment Generator (ESG) and Extreme Sentiment Classifier (ESC). ESG is a statistical method used to generate a standard lexical resource called ExtremesentiLex, containing only extreme positive and negative terms. This lexicon is then embedded in ESC and tested on five different datasets. ESC finds posts with extremely negative and positive sentiments in these datasets.
The result verifies that the posts previously classified as negatives or positives are, in fact, extremely negatives or positives in most cases. The COVID-19 pandemic has forced people to change their lifestyles, and Lockdown further pushed people to use social media to express their opinions and feelings. It provides a good source for studying users' topics, emotions, and attitudes discussed during the pandemic. The authors of work  (Abdukhamidov et al. 2021 ) collected two massive COVID-19 datasets from Twitter and Instagram. They explore data with different aspects, including sentiment analysis, topic detection, emotions, and geo-temporal. Topic modeling on these datasets with distinct sentiment types (negative, neutral, positive) shows spikes in specific periods. Sentiment analysis detects spikes in specific periods and identifies what topics led to those spikes attributed to economy, politics, health, society, and tourism.
Four sequence classification heads are added to the encoder, and the model is fine-tuned for multi-class classification tasks. The sentiment classification task categorizes tweets into positive and negative categories, while emotion classification classifies tweets into different emotion categories  (anger, disgust, fear, joy, sadness, surprise, enthusiasm, fun, hate, neutral, love, boredom, relief, none). The offense target is categorized as an individual, group, and unmentioned to others. Final classification detects HOF and classifies tweets into HOF and non-HOF. Kong et al. (2021)  explore a method that explains how extreme views creep into online posts. Qualitative analysis is applied to make ontology using Wikibase. It proceeded from the vocabulary of annotations such as the opinions expressed in topics and labeled data collected from three online social networking platforms (Facebook, Twitter, and Youtube).
In the next stage, a dataset was created using keyword search. The labeled dataset is then expanded using a looped machine learning algorithm. Two detailed case studies are outlined with observations of problematic online speech from the Australian far-right Facebook group. Using our quantitative approach, we analyzed how problematic opinions emerge. The approach exhibits how problematic opinions appear over time and how they coincide. Demszky et al. (2019)  highlight four linguistic dimensions of political polarization in social media: topic choice, framing, and affect an apparent force. These features are quantified with existing lexical methods. The clustering of tweet embeddings is proposed to identify important topics for analysis in such events. The method is deployed on 4. 4 M tweets related to 21 mass shootings.
In the final phases, those candidates are ranked using a masked language model called SpanBERT. Yang et al. (2018)  explore the use of Network Structure Information (NSI) for detecting human trafficking on social media. They present a novel mathematical optimization framework that combines the network structure into content modeling to tackle the issue. The experimental results are proven effective for detecting information related to human trafficking. Transfer learning is beneficial for various NLP tasks. However, negative transfer learning restricts the performance where the model solving an earlier problem makes later problems harder to solve. A study  (Minoofam et al. 2021)  proposes a transductive learning algorithm based on cellular learning automata (CLA) to deal with the issue of negative transfer (NT).
Event prediction Event prediction is a complex issue that revolves around many dimensions. Various events are challenging to predict before they become apparent. For example, it is impossible to predict in case of natural disasters, and they can only be detected after the occurrence. Some events can be predicted while they are still in the evolving phase. Authors of  Nourbakhsh et al. (2017)  identify events from local news sources before they may become breaking news globally. The use case of COVID-19 can be regarded as an example where it started locally and became a global issue later. A dataset is obtained from a recent Kaggle competition to explore the usability of a method for predicting disaster in tweets. The work in  Chanda (2021)  tests the efficiency of BERT embedding, an advanced contextual embedding method that constructs different vectors for the same word in various contexts.
Detecting incel can help us prepare for the possible worse scenarios. A comprehensive study  (Hajarian et al. 2019)  investigates the profile of people inclined towards incel and provides a dataset for incel detection. Similarly, people inclined toward the extreme right, radical and criminal agenda can also help us predict the events based on the detected information. Most of the other existing research targets particular scenarios of event prediction with limited scope. Keeping in mind the complexity of this problem, we only present a few related works, and the generalization is obscure.
Event detection datasets Due to the growth of the internet and related technologies, research in event detection has experienced significant interest and effort. However, the benchmark datasets for event detection witnessed slow progress. This can be attributed to the complexity and costliness of annotating events that require human input. There are a handful number of datasets available that covers event detection. These datasets are mostly limited to the small size of data and very restricted types of events. They address specific domains based on certain features. This also raises issues using a data-hungry deep learning model and typically requires balanced data for each class. Some of these datasets are briefed in the following paragraphs. Table  3  compares the discussed datasets and knowledge bases.
MAVEN  (Wang et al. 2020 ) which stands for MAssive eVENt detection dataset, offers a general domain event detection dataset manually annotated by humans. It uses English  Wikipedia and FrameNet (Baker et al. , 1998)  documents for building the dataset. It contains 111,611 various events and 118,732 events mentioned. The authors claim this to be the largest available human-annotated event detection dataset. There are 164 different events, representing a much wider range of public domain events. The event types are grouped under five top-level types: action, change, scenario, sentiment, and possession. EventWiki  (Ge et al. 2018 ) is a knowledge base of events, which consists of 21,275 events containing 95 types of significant events collected from Wikipedia. EventWiki gives four kinds of information: event type, event info-box, event summary, and full-text description.
Potential advantages and disadvantages The concept of dangerous events is theoretical at this stage and a great outcome is expected. However, it can only be established after backing it with the results obtained after the experimentation. There are some potential advantages that can be considered at this stage are enlisted below: • Creation of common base for all relates dangerous events. • Discovering a general purpose method that can detect the majority of dangerous events. • Construction of one of its kind comprehensive dangerous events dataset. • Possible assistance in relating various dangerous events happening in real-time. • Improving the ability to rank dangerous events according to the urgency of the immediate situation. There are some foreseeable disadvantages associated with this approach that is enlisted below.
Extreme events include extreme positive or extreme negative. However, dangerous events only fulfill extreme negative cases where there is a common wish to evade possible dangers posed to the safety of a person, group, or society. Detecting such dangerous events can ensure public safety while providing a broader view of the events happening. Various events happening in the virtual or real sphere are probably interrelated, while some might give rise to other dangerous events. Approaching the situation in a unified manner can give the advantage of prioritizing and acting in anticipation. Furthermore, it can lead to developing and discovering the best method for all such events. A dataset of dangerous events is crucial for the experiments, yet no specific dangerous event dataset exists. We believe there is an excellent scope for related work in the future.
Acknowledgements National Founding supported this work from the  FCT Fundacão para a Ciência e a Tecnologia  through the  MOVES  Project -PTDC/EEI-AUT/28918/2017  and co-financed by the  Portugal 2020 Program  ( PT2020 ) in the framework of the  Regional Operational Program of the Center (CENTRO 2020 ) and the  European Union  through the  Fundo Europeu de Desenvolvimento Regional (FEDER) , under the Grant  CENTRO-01-0247-FEDER-113066-"HORUS360-HORUS 360  • iOMS inteligente Offender Management System".
In Ref. [19] , we also reported an unexpected excess of events above the background model with energies < 200 eV ee. This paper revisits the same data and provides supplementary information for a robust exploration of the reported event excess. This paper is organized as follows. In Sec. II we summarize our experimental setup, including a detailed description of the DAMIC at SNOLAB detector, CCD sensor operation, and our data analysis. In Sec. III we expand on this discussion with a focus on backgrounds, as from radiocontaminants in detector materials and on their surfaces. We detail our application of the GEANT4 simulation package  [20]  to these background sources and our modeling of the partial charge collection region discovered near the back surface of the CCDs. In Sec. IV we present the construction of our radioactive background model from a fit to data above 6 keV ee with templates constructed from simulated events, as well as an independent cross check on the activity of surface 210 Pb and the extrapolation of this model into our WIMP-search region.
A. DAMIC detector The DAMIC detector at SNOLAB consists of an array of eight CCD modules in a towerlike configuration that was installed in January 2017. The topmost CCD module (CCD 1) was fabricated with ultralow radioactivity copper and is shielded from the others by ultralow radioactivity lead. One of the other eight CCDs was disconnected soon after installation due to luminescence from one of the amplifiers, which produced unwanted charge throughout the CCD array. Of the remaining devices (CCDs 1-7), one device (CCD 2) was initially not operational but unexpectedly came back online after a temperature cycle and electronics restart caused by an unplanned power outage at SNOLAB in April 2017. The DAMIC at SNOLAB CCDs were packaged by Fermi National Accelerator Laboratory (Fermilab). The CCD package consists of the CCD sensor and Kapton flex cable glued onto a silicon support frame cut from highresistivity silicon wafers from Topsil (of the same origin as those used for CCD fabrication).
For this analysis, we used a subset of DAMIC data where a variable in the readout software was modified so that 100 rows of the CCD pixel array were transferred into the serial register before the serial register was clocked and the pixel charge measured (referred to as 1 × 100 readout mode). This downsampling procedure at the readout stage results in an effective pixel size of 15 × 1500 μm 2. This procedure reduces the position resolution in the ŷ-direction but allows the charge from an ionization event (spread out over multiple rows) to be read out in a smaller number of measurements, substantially reducing the uncertainty in the total ionization charge (energy) from an interaction. With 1 × 100 readout, only a few percent of low-energy events populate more than one row in an image, which simplifies the analysis to one dimension (along rows), while retaining some coarse resolution in ŷ to study the spatial distribution of observed events.