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1,944 results for “Class I”
IMPROVING METHODS OF SOLVING PROBLEMS OF A PRACTICAL NATURE IN PRIMARY GRADE MATHEMATICS CLASSES
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DIFFERENT APPROACHES TO THE THEORY OF USING VIDEO MATERIALS IN ENGLISH CLASSES
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UTILISATION DE TEXTES PUBLICISTIQUES EN FRANÇAIS POUR LA FORMATION DES COMPÉTENCES SOCIO-CULTURELLES CHEZ LES CLASSES TERMINALES
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PROVIDING KNOWLEDGE ABOUT SYLLABLES IN THE MOTHER TONGUE TEXTBOOK FOR CLASS 2
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Pep-TCRNet: Prediction of Multi-Class Peptides by T-cell Receptor Sequences with Deep Learning
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THE SPECIFIC FEATURES OF TEACHING DRAMA IN ENGLISH LITERATURE CLASSES: CLASSIC AND MODERN PLAYS
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THE THEORY OF THE VERB WORD CLASS IN MODERN ENGLISH
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Optimizing Class Integration Testing with Criticality-Driven Test Order Generation
<p>The generation of class integration test orders (CITOs) is a pivotal element in integration testing, which focuses on determining the optimal order for integrating classes while testing an object-oriented system. Due to a high number of dependencies and their possible error proneness, some classes are more critical than others in a program. Existing methods for handling these classes only assess risk in terms of their dependencies; they do not consider historical bug information as an additional indicator and mainly work on small programs. </p> <p>To overcome these limitations, this paper introduces Criticality-Driven CITO (CD-CITO) generation, an innovative approach to optimize CITOs by focusing on class criticality. CD-CITO assesses both the importance of a class in terms of its dependencies and the likelihood of defects, based on historical bug data, to determine a criticality score. Then, it reformulates the CITO generation problem as a reinforcement learning task and uses the Advantage Actor-Critic (A2C) algorithm to address it. We propose a novel reward calculation strategy to guide the learning agent, balancing stubbing costs with the criticality values of classes to optimize the test order. To extract fault proneness information and access the approach, the paper uses Defects4j, a data set that contains real bugs and patches of Java programs. The results obtained show that CD-CITO effectively identifies and prioritizes highly critical classes and also minimizes stubbing costs while generating CITOs, which makes it a valuable tool for integration testing.</p>
Mapping the páramo land cover in the Northern Andes: Figure S4 Expert land-cover classification of the Andean páramo and distribution according to three groups: natural vegetation, natural abiotic and anthropogenic, and 12 classes
<p>The Andean páramo is a biodiverse and vulnerable tropical high-mountain region, whose spatio-ecological patterns remain understudied. The lack of general characterization of its overall extent, land-cover classes, and treeline spatial features hinders our capacity to understand its responses to human impacts and predict future land-system changes. To address this knowledge gap, we classified the land-cover of the páramo in the northern Andes. Moreover, we estimated 1) the páramo's total extent and distribution among countries, 2) the relative extent of 12 of its main land-cover classes, categorized into <i>natural vegetation, natural abiotic</i> and <i>anthropogenic </i>groups, and 3) the preliminary position and anthropogenic influence of its bordering treeline. Relying on Landsat 8 imagery, we performed hybrid manual-automated classifications using the Maximum Likelihood and Random Forest algorithms. The two resulting <i>final classifications</i> were manually checked for errors compared to Google Earth and VegPáramo data, and used to produce the <i>expert classification</i>. Finally, we delimited the treeline based on regional forest connectivity, and applied it to the expert classification to evaluate páramo elevations, surface areas and land-cover classes above the treeline. The páramo extent was estimated at 24,301 km<sup>2</sup>, distributed between Ecuador (47%), Colombia (43%), Venezuela (8%) and Peru (2%). Natural vegetation, especially shrublands, rosette plant communities and grasslands were dominant (altogether, 65%), whereas classes reflecting intense land-use covered 12% overall. The average treeline reached 3546 m and was bordered uphill at 16% with anthropogenic land-cover classes. The páramo's extent is smaller than previously suggested. It remains a (semi-) natural region, yet crop and pasture expansion towards high elevations is a critical concern for long-term sustainability. Future research can build on our findings to predict land-system changes and assess priority areas for conservation. We recommend for future research to focus on remnant forest patches and treeline connectivity in priority.</p>
native non-endemic flora for each abundance class. [n = total in each category; D? = probably extinct; RR = very rare; R = rare; PF = infrequent or disseminated; LOC = localised; C = common; CC = very common] in Floristic analyses of the Corsican flora: biogeographical origin and endemism
native non-endemic flora for each abundance class. [n = total in each category; D? = probably extinct; RR = very rare; R = rare; PF = infrequent or disseminated; LOC = localised; C = common; CC = very common]
Parallel processing by distinct classes of principal neurons in the olfactory cortex
<p>Understanding how distinct neuron types in a neural circuit process and propagate information is essential for understanding what the circuit does and how it does it. The olfactory (piriform, PCx) cortex contains two main types of principal neurons, semilunar (SL) and pyramidal (PYR) cells. SLs and PYRs have distinct morphologies, local connectivity, biophysical properties, and downstream projection targets. Odor processing in PCx is thought to occur in two sequential stages. First, SLs receive and integrate olfactory bulb input and then PYRs receive, transform, and transmit SL input. To test this model, we recorded from populations of optogenetically identified SLs and PYRs in awake, head-fixed mice. Notably, silencing SLs did not alter PYR odor responses, and SLs and PYRs exhibited differences in odor tuning properties and response discriminability that were consistent with their distinct embeddings within a sensory-associative cortex. Our results therefore suggest that SLs and PYRs form parallel channels for differentially processing odor information in and through PCx.</p>
Class-aware Sounding Objects Localization via Audiovisual Correspondence
<p>Videos for "Class-aware Sounding Objects Localization via Audiovisual Correspondence"</p>
Class-aware Sounding Objects Localization via Audiovisual Correspondence_mp4
<p>Class-aware Sounding Objects Localization via Audiovisual Correspondence</p>
Tests generated by Randoop for class integration test generation
<p>Tests generated by Randoop for class integration test generation experiment</p>
Open Science materials of the paper "Automatically Recognizing the Semantic Elements from UML Class Diagram Images"
<p>This submission contains such files:</p> <p>1. "questionnaire.docx": The questionnaire for the survey. The file contains all the questions and answers.<br> 2. "raw data collected from participants.xlsx": The raw data collected from participants. Each row in the file represents a participant's answers to all questions, including the date, source, IP, and answers.<br> 3. "raw data collected from open-source community.xlsx": The raw data collected from open-source community (the UML diagram usage). It contains the repositories and GitHub URLs, the UML diagrams and the corresponding links, and some statistics about the UML diagram usage.<br> 4. "an implementation of ReSECDI.zip", "utility source code.zip", "utility compiled JAR.zip", and ".m2.zip": An implementation of ReSECDI, and its dependencies. The implementation is in Java, and it requires JDK11 or higher. It depends on a project named "utility", in addition to other dependencies. The source code of "utility" is provided in "utility source code.zip", the compiled JAR file is in "utility compiled JAR.zip", and the maven dependency files are provided in ".m2.zip". The ways to add the "utility" to the implementation's dependencies are explained in the "readme.txt".<br> 5. "instructions for how to use the artifacts.docx": The instructions for how to use the implementation of ReSECDI. It mainly explains the key components of the implementation, and how to set the parameters.<br> 6. "diagrams used for its evaluation.zip": The diagrams used for the evaluation. There are 50 diagrams collected from the open-source community. Each diagram's name represents its belonging repository.<br> 7. "raw data collected during the evaluation.xlsx": The raw data collected during the evaluation. It contains the statistics of the classes and relationships for each diagram, and the recognition results.<br> 8. "Manuscript.pdf": The manuscript explaining our approach.<br> 9. "readme.txt": The readme file explaining details about each file.</p>
Data from: Correlated variability in primate superior colliculus depends on functional class
<p><span>Information read-out from populations of neurons depends strongly on the correlated variability within a population, termed r<sub>SC </sub>(spike count correlations). Traditionally, r<sub>SC</sub> is reported as a single value for a population of neurons comprising a brain area. However, single values, like summary statistics, can obscure underlying features of the constituent elements. We predict that in brain areas consisting of distinct subpopulations of neurons, different subpopulations will exhibit distinct levels of r<sub>SC</sub> not captured by the population r<sub>SC</sub>. We tested this idea by recording from neurons in macaque superior colliculus (SC), a structure that contains several subpopulations (or classes) of neurons based on function. We found that during guided saccade tasks, different classes of neurons exhibited differing degrees of r<sub>SC</sub>, which could not be attributed to variations in behavior. Neurons belonging to the "Delay" class displayed a particularly high r<sub>SC</sub>, especially during the delay epoch of tasks that relied on visual working memory. Our finding that r<sub>SC</sub> depends on functional class, as well as the type of task, demonstrate the importance of taking functional subpopulations into account when attempting to model or infer population coding principles in primate SC, or other areas of the brain that contain subpopulations supporting specific functions.</span></p>
DEVELOPMENT OF LEARNING COMPETENCIES OF FUTURE PRIMARY CLASS TEACHERS
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ENHANCING READING COMPETENCE IN ENGLISH LEARNING CLASSES
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Fig. 2 in A report on 24 unrecorded bacterial species of Korea isolated in 2016, belonging to the orders Rhizobiales and Sphingomonadales in the class Alphaproteobacteria
Fig. 2. Neighbor-joining phylogenetic tree based on 16S rRNA gene sequences showing the relationship between the strains isolated in this study and their relatives in the Rhizobiales of the Alphaproteobacteria. Bootstrap values (expressed as percentages of 1000 replications) over 70% are shown at nodes for neighbour-joining, maximum parsimony, and maximum likelihood methods, respectively. Filled circles and open circles indicate that the corresponding nodes were recovered by all treeing methods and by two treeing methods, respectively. Bootstrap values (>70%) are shown at nodes. Bar, 0.02 substitutions per nucleotide position.
Figs. 15–17. 15. Melania laeta Jay, 1839 in Catalog Of Recent Type Specimens In The Division Of Invertebrate Zoology, American Museum Of Natural History. V. Mollusca, Part 2 (Class Gastropoda [Exclusive Opisthobranchia And Pulmonata With Supplements To Gastropoda [Opisthobranchia], And Bivalvia
Figs. 15–17. 15. Melania laeta Jay, 1839 (syntype—AMNH 56113), X 1.5. 16. Odostomia nestho) beauforti Jacot, 1921 (holotype—AMNH 15728), X 8. 17. Turbo rotelliformis Jay, 1839 types—AMNH 56071 [right], 56071a [left]), X 1.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.