dataesr/scientific-paragraphs-categorization huggingface dataset:
Relevance for Teaching and Learning Clearly, the primary drivers for increased adoption of OER are affordability, access, and digital equity. Reducing or eliminating resource costs can have a profound impact on student recruitment and retention. Some institutions market OER as a value-add for attending their campus. OER may also provide faculty more agency in what learning materials to adopt and use. Despite the obvious advantages that OER provides, challenges remain. Even though we see evidence of a growing corpus of resources, nearly seventy-three percent of students and fifty-six percent of faculty have never heard of OER, though those numbers are improving. Additionally, "OER" is often conflated with "e-textbooks" and subscription databases that might or might not be open. Clearly, institutions have much work ahead in educating both the faculty and students.
M any of the findings in the 2020 Horizon Report contain implications for Australian higher education. For example, the emerging trends of adaptive learning technologies, artificial intelligence and machine learning educational applications, and analytics for student success imply that Australian higher education needs to be developing new tools, approaches, and methods of data science applied to technologyenhanced learning and teaching. There are 43 universities in Australia, 40 of which are public universities. The country's higher education institutions have the highest ratio of international students per population in the world by a large margin, with 812,000 international students enrolled in the nation's universities and vocational institutions. Further, the Organisation for Economic Co-operation and Development (OECD) ranks Australia fourth in the world for high-quality research, adding to its attraction for students as well as researchers.
In Australia, interest is rising in the transformative and disruptive potential of data science as a new scientific method. In this context, data science includes global computational resources as co-production partners in exploratory and confirmatory research, as well as drivers of innovation, for example, to solve the challenge of personalization at scale. Data science brings a focus on using advanced computing to create and test complex models of the processes and outcomes of learning and teaching. Learning analytics, for example, uses data science methods with static and dynamic information to model the processes and outcomes of learners interacting within digital learning environments-aggregating, assessing, and analyzing that information for real-time prediction and optimization of learning processes, learning environments, and educational decision-making.
Relevance to the Challenges Facing Higher Education in Australia The purpose of driving innovation and improvements in learning and teaching in higher education with data science and learning analytics is to better understand the concepts and mechanisms of learning and to enable effective new interventions and methods involved in teaching, research, and creating positive impacts in communities. Four considerations underscore the relevance of transforming research, teaching, and policymaking guided by the emerging themes in the 2020 Horizon Report-in particular, the implications for data science and learning analytics. • Literacy, fluency, and control over data are linked. As fluency with data becomes a vital skill, the effect of disintermediation-the removal of intermediaries of time, complexity, and access between the user and the data needed for better decision-making-creates a positive feedback cycle for increased control and autonomy.
To leverage the relationship between instructional design and learning analytics, and to extend to course and curriculum analytics (such as through AI): Researchers and practitioners can use learning analytics to inform the advancement of instructional design for quality learning, teaching, and assessment. To understand the impacts of combining data types from all sectors (health, socioemotional, socioeconomic status, etc. ) on interactions with people to improve data models and leverage AI and related technologies: Everyone has a role to play to ensure that the control and ownership of data are clear, transparent, and in the hands of the person who is the subject of the data (e. g. , EU-GDPR, ISO standard on privacy). Higher education institutions that respond to the implications of the 2020 Horizon Report with key actions such as these strengthen their institutions while helping to advance the science and practice of learning and teaching.
Arabic is more complex to treat with natural language processing than Latin text, although open-source algorithms have been adapted to work on Arabic data. AI can mitigate but also exacerbate inequalities and reproduce biases unless all stakeholders are involved in policy making and the creation and testing of algorithms. The Open AIR project is researching AI ethics, including gender equality, youth employment, and participation of marginalized groups in the formation of African AI policies. Interest has also been growing in teaching AI in universities in Egypt and UAE. Egypt has an AI policy in progress, which hopefully will ensure that technology is not used "to [further] marginalize" people, and AI policies must ensure that data collection does not promote surveillance or infringe upon privacy and civil liberties.
For more information about community colleges, see the American Association of Community Colleges and the League for Innovation in the Community College. This sector is perhaps the most diverse in US higher education in terms of institutional mission, size, academic profile, and student demographics, making generalizations difficult. Yet it is clear that baccalaureate institutions of all types must reckon with two significant challenges: accelerating changes in student demographics, and the issues raised by the increasing use of data analytics in strategic planning and regular operations.
These larger trends-and the current evidence and anticipated impacts of these trends-served as the grounds on which the panel built its discussions on the emerging technologies and practices influencing postsecondary teaching and learning. As they provided their inputs and engaged one another in discussion, panelists were encouraged to share news articles, research, and other materials that would help reinforce their inputs and provide evidence for their particular viewpoints on current and future trends. In addition to enriching the panel's discussions and supporting the panel's voting and consensus processes, these materials were collected by EDUCAUSE staff for use as evidence and further reading in the writing of this report. In the Delphi and IFTF methodologies, these collected materials also serve the purpose of ensuring that the panel's future forecasts are sufficiently grounded in "real" data and trends, not merely science fiction.
Thin single crystalline film (SCF) scintillators are essential when performing high resolution X-ray imaging with micron to sub-micron resolution. Especially when high energy X-rays are required for the experiment, the absorption efficiency is reduced considerably due to the limited thickness of the SCF. The absorption efficiency can be maximized by tailoring the SCF to have a high density and effective Z number. However, the quest to find these optimized scintillators is both time consuming and expensive when performing material screening. By combining simulations performed using the Geant4 package with subsequent analytical calculations, we propose an efficient simulation tool. Geant4 simulations predict the spatial distribution of the deposited energy in the SCF and the analytical calculations mimic the blurring introduced by the microscope optics.
Energy deposition As discussed above, achieving ultimate spatial resolution (B1 mm or better) requires SCFs of thickness thinner than 10 mm rendering the X-ray absorption critical, especially when applying high energy X-rays. The X-ray absorption efficiency affects the Detective Quantum Efficiency (DQE) of the system and should therefore be considered when examining the overall performance of SCF potential scintillators. In low dimensional systems, the effective energy deposition differs from the attenuated energy due to the escape of X-ray fluorescence and even hot electrons and Auger electrons in the particular case of nanoscintillators. 30, 31 The effective energy deposited in the investigated SCFs has been extracted from our simulations at X-ray energies from 15 to 100 keV (Fig. 2b ). Its comparison to the attenuated energy in the freestanding SCFs using the photon cross section database from NIST  32  (Fig.
Influence of X-ray fluorescence We performed simulations of the standard SCF, namely LAG:Ce supported by YAG substrate to highlight the effects on the LSF and MTF when imaging is performed with X-ray energies below and above the K-edge of the heaviest element of the substrate (resp. the SCF), here Y at 17. 04 keV (resp. Lu at 63. 31 keV). As shown in Fig. 3b , increasing the X-ray energy above the substrate K-edge of Y initiates a sharp decrease of B20% in Fig. 2  (a) Percentage of the incident beam energy attenuated by various kinds of 5 mm thick SCFs, calculated using data from the NIST database. 32 b) Percentage of the incident beam energy deposited in various 5 mm SCFs supported by 150 mm substrates, calculated by tracking all the secondary X-rays and electrons using our application based on the Monte Carlo Geant4 toolkit.
Using Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, we have identified that neurogenesis-related genes were prominent among DEGs at 1 dpl. While 3 dpl was marked by the genes related to immune response, cell proliferation and apoptosis, genes with key roles in neuronal differentiation and the Notch pathway were abundant among DEGs at 14 dpl. Weighted gene coexpression network analysis (WGCNA) of three regeneration stages revealed twelve distinct co-expression modules, nine of which were specific to a particular stage. Moreover, gene modules and gene expression profiles at 1 dpl and 3 dpl were unique, while those at 14 dpl are rather similar to the control group. Next, we have compared the whole transcriptomes of the regenerating brain at the three stages to those of the human adult brain tumors low-grade glioma (LGG) and glioblastoma (GBM).
Primer pairs with the R 2 values equal to or greater than 0. 99 and an efficiency falling in the acceptable range (90-110%) were used in the qPCR reactions. Data were analyzed with the GraphPad Prism 8 software (Graphpad Software Inc. , CA, United States). The values are indicated as mean ± SEM (Standard Error of Mean) of triplicates. Primer sequences for the tested zebrafish genes are provided in Supplementary Table  S1.
Transcriptomic Analyses of Zebrafish Brain Regeneration and Human Brain Cancers Read quality control of each zebrafish brain RNA-seq sample was initially performed by using the FastQC tool  (Andrews, 2010). The reads were aligned to the zebrafish reference genome GRCz11 (danRer11) using HISAT2 (version 2. 1. 0) (Kim et al. , 2015). After mapping, transcripts were counted with HTSeq 0. 6. 0 tool by using the annotation file Danio_rerio. GRCz11. 93. gtf obtained from the Ensembl  (Anders et al. , 2015). Normalization and transformation (vst) of the read counts, as well as differential expression analysis, were performed by using DESeq2 package (version 1. 28. 1) of Bioconductor  (Love et al. , 2014). Principal component analysis (PCA) and sample-tosample distance analysis were conducted to check data and plots were visualized by using ggplot2 (version 3.
Weighted Gene Co-Expression Network Analysis We ran WGCNA on a filtered and transformed expression matrix of the zebrafish brain regeneration dataset. Raw counts were transformed using the variance-stabilizing transformation (vst) of the R package DESeq2  (Love et al. , 2014)  as recommended by the WGCNA manual  (Langfelder and Horvath, 2008). Genes with less than 10 counts in more than 90% of the samples were filtered for subsequent analysis. After this filtering, 22,853 genes were fed to WGCNA for the regeneration dataset. Network was constructed using unsigned co-expression similarities between genes. As opposed to signed co-expression, unsigned coexpression conserves similarity between highly correlated genes, even in the case of negative correlation. Unsigned coexpression similarity between two genes i and j is defined as the absolute value of their sample correlation: s i,j |cor(x i , x j )|.
A soft threshold (also called power) of 9 was picked due to the sample size (n = 18) accepted as small according in order to construct a co-expression network. The soft threshold β expresses the way the co-expression similarity translates into an adjacency weight in the network: a i,j s β i,j. The higher the soft threshold, the further weak co-expressions are pushed towards 0, although without being made equal to 0, i. e. , soft thresholding. For zebrafish brain regeneration data, a power of 9 was chosen by default, due to the sample size (n = 18) accepted as small according to the WGCNA manual  (Langfelder and Horvath, 2008). A weighted co-expression network was constructed using these parameters. Gene modules were then delineated from the clustering using the dynamic hybrid tree cut algorithm with a deep split parameter of 2 and a minimum cluster size of 100.
Collection of Brain Cancer Samples' Data from The Cancer Genome Atlas RNA-seq data of adult human GBM and LGG samples were obtained from the TCGA data portal (National Cancer Institute, 2020). TCGA defines LGG as tumors of grades II and III based on standards set by the World Health Organization (WHO). The "Level 3" gene expression data for all TCGA-LGG (529 LGG samples and 4 control samples) and TCGA-GBM (165 GBM samples and 5 control samples) samples were downloaded from the TCGA database.
Functional Annotation The lists of significantly altered genes acquired from individual comparisons were used as inputs of functional analyses for the database for Annotation, Visualization and Integrated Discovery (DAVID version 6. 8)  (Huang da et al. , 2009). When comparing stages of zebrafish brain regeneration and human cancers, lists of shared or exclusive genes were built using human orthologs of zebrafish genes (Supplementary Table  S7 ). For comparisons within the zebrafish model, the original gene identifiers were used. For functional enrichment, the ease score, a modified onetailed Fisher's exact test, was used to determine the enriched Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways by means of a user-defined gene list for each annotated DAVID GO term and KEGG pathway.
Functional enrichment was performed according to biological domains of GO terms with respect to three aspects: biological process (BP), molecular function (MF), and cellular component (CC). Gene lists obtained from the AmiGO database  (Carbon et al. , 2009)  and manually curated as related to the selected functions (Figure  2A ) were plotted using the R package pheatmap (version 1. 0. 12) (Kolde, 2012). Gene lists related to the selected KEGG pathways were obtained from the KEGG database and plotted using the GOplot package  (Walter et al. , 2015). Significantly enriched GO terms and KEGG pathways were plotted using the package ggplot2  (Wickham, 2016).
Gene Co-Expression Network Analysis Reveals Divergence from Control in Early Stages of Brain Regeneration and Convergence to Control at Late Stages Next, to explore the co-expression relationship between different gene sets, we performed weighted gene co-expression network analysis (WGCNA) on 1, 3 and 14 dpl samples and identified twelve distinct groups of co-expressed genes, the so-called modules (Supplementary Figure  S3 , Supplementary Table  S5 ). Expression of the genes clustered in nine modules (M1-M5 and M7-M10) showed a stage-specific component (Figure  3A , Supplementary Figure  S3B ), i. e. , the genes in these modules revealed expression patterns that distinguished one stage of regeneration from the others, indicating a grouped response peaking at that particular stage. Notably, GO term enrichment analyses performed by using the genes clustered in   6,123, 3 dpl: 4,662, 14 dpl: 1954)  were used for the analyses.
The heatmap scale shows log 10 of the ease p-values for the most significantly enriched GO terms. (E) Relative expression levels of genes that are Down or Up at different stages of regeneration. bdnf is Down at 3 dpl and 14 dpl, while syt2a and epha6 are Down at all stages. capgb is Up at all stages, while gfap is Up at 1 dpl and 3 dpl. Statistical significance was evaluated using unpaired t-test. *p < 0. 05, **p < 0. 01 and ***p < 0. 001. ns: non-significant. Error bars represent ±standard error of mean (SEM, n = 3). Up: upregulated, Down: downregulated, dpl: days post-lesion, DAVID: database for Annotation, Visualization and Integrated Discovery, GO: Gene Ontology, BP: Biological Process. these nine modules showed a similar pattern with that performed by using the DEGs for each stage in BP category (Figure  3B , Supplementary Table  S6 ).
The Early Wound Healing Stage of Brain Regeneration is More Similar to Low-Grade Glioma and Glioblastoma than the Proliferation and Differentiation Stages While individual comparisons of the regenerative stages to LGG and GBM are informative about particular similarities of these stages to gliomas, a global comparison is necessary to reveal which stage of brain regeneration is most comparable to brain cancer. To this purpose, we drew heatmaps of log 2 fold changes of the 3,615 genes that are differentially expressed in at least one stage of brain regeneration and shared with at least one type of brain cancer (Supplementary Figure  S8 ). The genes obtained from the KEGG database included a substantial number of genes involved in glioma, pathways in cancer, as well as Wnt, p53, JAK-STAT Notch, apoptosis, RAS, MAPK, mTOR and PI3K-Akt signaling pathways (Figures  7A, B , Supplementary Figure  S9 ).
The heatmap scale shows log 10 of the ease p-values for the most significantly enriched GO terms. dpl: days post-lesion, DAVID: database for Annotation, Visualization and Integrated Discovery, GO: Gene Ontology, BP: Biological Process, MF: Molecular Function, CC: Cellular Component, dpl: days post lesion, LGG: low-grade glioma, GBM: glioblastoma.
FUNDING This work was supported by the  Scientific and Technological Research Council of Turkey (TUBITAK , grant number  215Z365 ). GO Lab is funded by  EMBO   Installation Grant  ( IG 3024 ). YD was supported by a  TUBITAK   2214-A International Research Fellowship Program  for PhD Students. YD and EK were supported by  TUBITAK   2211-C Domestic Priority Areas Doctoral Scholarship Program.
Introduction Linear Discriminant Analysis (LDA) and its kernel extension (KDA) are efficient dimensionality reduction tools allowing the classification to multiple categories in datasets. There are used in numerous areas as diverse as speech and music classification  [1]  , video classification  [2]  , outlier detection  [3]  , supervised novelty detection  [4, 5]  , etc. Their attractivity relies on the fact that both are formulated as a Rayleigh quotient whose the solution is easily obtained by solving an equivalent generalized eigenvalue problem (GEP). KDA generalizes LDA to nonlinear data sets by using the famous kernel trick. Specifically, KDA is based on the construction of a kernel matrix and the KDA's solution results from the eigendecomposition of this kernel matrix. With database volume that increases more and more, the storage requirement and the computational cost involved by KDA make impractical its use with real modern data sets.
b) Secondly, the proposed method remains however limited to moderate sized problems because there are faced with an everincreasing kernel Gram matrix when dealing with streaming data. To remedy this situation, we show that the null space constraint of KNDA can be used as an efficient criterion of compression by identifying redundancy in the training data set. The compression rate of the proposed method is monitored by an user-defined compression factor. The combined action of these two steps offers a significant gain in terms of time complexity and memory burden while offering comparable classification performance and learning time with state-of-the-art incremental learning methods. c) And third, we address the problem of novelty detection both in the multi-class and one-class scenarios. In particular, we show that incremental learning with KNDA implies a contraction of the distances in the null space.
INKDA with compression: ICNKDA Our incremental NKDA uses all training samples in the learning stage. But for very large scale datasets, our algorithm is faced two problems: a memory overhead to store the entire kernel Gram matrix and a growing computational cost corresponding to its eigendecomposition. The question that is addressed here is: can some learning data be deleted without significantly affecting the quality of the solution that would be obtained from all data? The idea we propose here is to remove redundancy in data by using the properties of the null KDA. Let us assume that C -1 null projection di- rections x = π x 1 ,. , π x C-1 have been learned from n previously seen data vectors X = ( x 1 ,. , x n ) , then we have ( x ) S x w x = 0. Consider now a sample x from X and m its class label ( ∈ { 1 ,.
Data sets and algorithms Table  1  provides a detailed description of the datasets which will be used to evaluate our method. These data sets involve several areas such as handwritten digit recognition, facial image recognition, document recognition,. More precisely, the MNIST dataset is a large collection of 28 × 28 sized gray level images for handwritten digit 0-9. The CIFAR-10 dataset is a large collection of 32 × 32 sized color images representing 10 different categories such as airplanes, cars, birds, cats, deers, dogs, frogs, horses, ships, and trucks. The extended YaleFaceB dataset is composed of 2432 images of 38 human subjects and around 64 near frontal images under different illuminations per individual. RCV1 is an archive of over 80 0,0 0 0 manually categorized newswire stories made available by Reuters, Ltd.
for research purposes. In our study, we use a subset of the RCV1 corpus which consists of 9625 documents with 29,992 distinct words, including categories "C15", "ECAT", "GCAT", and "MCAT". Lastly the AWA2 dataset consists of 37,322 color images of 50 animals categories. For MNIST and YaleB, we use the image pixels as features. Features are normalized in [0,1]. RCV1 is a sparse data set and no preliminary treatment is conducted. For CIFAR-10 and AWA2, we follow the experimental setup used in the literature (  [8, 9]  ). We extract learned image features from a pretrained convolutional neural network and use those features to train the studied classifiers. In this experiment, we choose the deep 101-layer ResNet pretrained network and features are extracted from the fully connected 'pool5' layer (2048 features).