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189 results for “feature model”
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. Tussock tundra nitrogen and phosphorus fertilization simulation
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for 25 years of tussock tundra under nitrogen and phosphorus fertilization conditions.
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. Tussock tundra phosphorus fertilization simulation
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for 25 years of tussock tundra under phosphorus fertilization conditions.
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. Tussock tundra shade house simulation
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for 25 years of tussock tundra under shade conditions.
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. Undisturbed tussock tundra
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for 100 years of undisturbed tussock tundra. Data is presented for day 250 of each year.
NEMO: A Tool to Support Feature Models with Numerical Features and Arithmetic Constraints
<p><strong>Your can watch this video in my Youtube channel:</strong></p> <p><a href="https://youtu.be/V-ONW8PftwM">https://youtu.be/V-ONW8PftwM</a></p> <p><strong>This is a Live Conference Presentation, please access and cite the published version of the respective publication:</strong></p> <p><a href="https://youtu.be/V-ONW8PftwM">https://doi.org/10.1007/978-3-031-08129-3_4</a></p> <p>Real-world <em>Software Product Lines</em> (SPLs) need <em>Numerical Feature Models</em> (NFMs) whose features not only have boolean values satisfying boolean constraints, but also have numeric attributes satisfying arithmetic constraints. A key operation on NFMs finds near-optimal performing products, which requires counting the number of SPL products. Typical constraint satisfaction solvers perform poorly on counting.</p> <p>Nemo (<strong>N</strong>umbers, f<strong>e</strong>atures, <strong>mo</strong>dels) supports NFMs by <em>bit-blasting</em>, the technique that encodes arithmetic as boolean clauses. Nemo translates NFMs to propositional formulas whose products can be counted efficiently by #SAT solvers, enabling near-optimal products to be found. We evaluate Nemo with a diverse set of real-world NFMs, complex arithmetic constraints, and counting experiments in this paper.</p>
Input features of E. coli proteome for predicting and modeling protein-protein interactions with AF2Complex
<p>Input features to be used with AF2Complex for predicting protein-protein interactions among ~4400 E. coli proteins. A pickled feature file was generated by the feature data pipeline of AF2Complex for each E. coli protein. To reduce storage size, we limited up to 10,000 MSA sequences and up to 10 structural templates from the Protein Data Bank. The cutoff date for sequence libraries and the Protein Data Bank releases used for feature generation is no later than 11-30-2021.</p> <ul> <li>ecoli_af2c_fea.txt -- A list of all E coli protein with pre-generated input features</li> <li>af2c_fea_ecoli_220331_msa10ktem10.tar -- Input features named after the UniProt ID of each proteins. Note that after untar the tarball, you may use the gzipped feature pickle files directly with AF2Complex w/o gunzip.</li> </ul>
Annotation of inverted repeats displaying features of pble STIR or IR in the hg38 genome model
<p>Annotation of inverted repeats displaying features of pble STIR or IR in the hg38 genome model. The annotation of <em>pble</em>-like inner inverted repeats was done using Palindrome (EMBOSS package). The output file was then filtered using pal2gff (https://github.com/Leelouh/pal2gff/blob/main/pal2gff.py), using as parameters a repeat size between 5 and 15 nucleotides, a spacer between pairs of inverted repeats (IRs) of 2 to 10 nucleotides, and a number of mismatches within repeats ranging from 0 to 1. These parameters were chosen taking into account those of the inner IRs found at ends of invertebrate pbles.</p>
The Kconfig Variability Framework as a Feature Model: Sampled Configurations for Manual Evaluation
<p>This dataset contains plain text files with sampled solutions used during the manual evaluation of the transformation rules presented in https://doi.org/10.5445/IR/1000162110. To reproduce the manual evaluation process yourself, please copy over the respective Kconfig files in a local copy of the Linux kernel Git repository and run `make menuconfig`. You need to insert an invisible `MODULES` configuration symbol to ensure that tristate configuration symbols are handled correctly by Kconfig. Additionally, you need to remove the default Linux Kconfig file and rename the Kconfig file for which you want to reproduce the evaluation process accordingly (simply remove the number prefix).</p><p>Configurations marked with KCONFIG_NONSOLUTION cannot be reconstructed in `menuconfig`, wherein configurations marked with KCONFIG_SOLUTION should be reproducable in the `menuconfig` interface.</p><p>We additionally provide the generated feature models for the 9 selected Kconfig files, alongside with the Kconfig files themselves. Kconfig{1,2,3,4,5} can be automatically evaluated with Kfeature, as they contain no tristate confsyms.</p><p>The upstream version of Kfeature can be found on Codeberg: https://codeberg.org/6b6279/Kfeature</p>
Capturing features of hourly-resolution energy models through statistical annual indicators
<p>Dear colleagues,</p> <p>This is the official repository of the Task 7.4 of H2020 Locomotion project. Feel free to use our data by citing this work and comment about our work by referencing the main authors of it. The article explaining this work is under revision. it will be referenced as soon as posible.</p> <p><strong>Python scripts </strong>("create_inputs.txt" and "run_simulations.txt") creates the input files for EnergyPLAN. The second one runs iteratively EnergyPLAN to generate the outputs of combinations (which are saved in the "EU_Iterate_case.xlsx" file). Hourly distributions of demands and supply technologies are contained in the RAR file ("EUdist.rar") and "EU_start_v2_noFlex.txt" initialize the starting configuration of the European energy system. Those files are required to run EnergyPLAN. The <strong>PowerPoint file</strong> ("EnergyPLAN_instructions.pptx") explains the procedure to carry out the runs of combinations in Python/Excel.</p> <p>In case you couldn't properly do the combinations, the<strong> Excel file</strong> ("EU.xlsx") saves this information, so the steps of the approach could be followed from this point with the Excel file. We have used Power Query (Excel) to prepare the data for the next step of building the regression models.</p> <p>The <strong>Matlab file (</strong>"CreateRegressionModels.m"<strong>)</strong> automatically generates the regression models for the European region of WILIAM (official model of the Locomotion project).</p> <p>Best regards,</p> <p>Gonzalo.</p>
Linear Kinematic Feature detected and tracked in sea-ice deformation simulationed by all models participating in the Sea Ice Rheology Experiment and from RGPS
<p>Linear Kinematic Features (LKFs) detected and tracked in sea-ice deformation fields simulated by sea-ice models participating in the Sea Ice Rheology Experiment (SIREx), a model intercomparison project of the Forum of Arctic Modeling and Observational Synthesis (FAMOS). These data are the basis of the feature-based evaluation of sea-ice deformation in Hutter et al., Sea Ice Rheology Experiment (SIREx), Part II: Evaluating linear kinematic features in high-resolution sea-ice simulations, Journal of Geophysical Research: Oceans (2022). This paper also provides further details on the parameters of the LKF extraction.</p> <p>The LKF data sets in this archive are stored in a csv-files for each year (1997 and/or 2008), which use semi-colons as delimiters. Each row corresponds to a pixel that was identified as LKF and the following information for this pixels is stored: Start Year, Start Month, Start Day, End Year, End Month, End Day, LKF No., Parent LKF No., lon, lat, ind_x, ind_y, divergence rate, shear rate. All pixels belonging to the same LKF have the same LKF number. Tracked LKFs are linked by the parent LKF number, where "0" denotes LKFs that newly formed. Detailed information on all variables is provided in the additional notes.</p>
Feature Extraction Using Hidden Markov Model for a Phonetic Process
<p>Speech is one of the primary forms of communication among humans. In real life, a dictionary is used to seek the pronunciation of a complex word; but, for computers, this look-up table is called a phonetic dictionary. A speech recognition process tags a word-utterance to its phoneme structure, thereby returning the grapheme representation. However, the speech recognition process is challenging because of the contextual relationship between words and sentences, dependent on speakers’ intentions. Further, factors influencing time, accents, noisy environment, and data security impose accuracy threats. The present research study proposes a new hybrid speech recognition model by considering three significant aspects: sound generation through phonetic representation, sound acoustics for transmission, and sound reception on how the sound is received. These steps are achieved through a speech-to-text model divided into various stages such as noise removal, speech-pause detection, feature extraction through framing, and windowing by adopting Hidden Markov Model (HMM). The implementation is performed on a phonetic tool, Praat. The robustness of the model is estimated using evaluation metrics such as f-measure and accuracy, resulting in 98% and 99% scores, respectively. Thus, the proposed approach efficiently transforms the spoken words into their corresponding text.</p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 6. The result of building a 3D model based on RF and SVM classification with "Important features".
<p>From the chart of figure 6, we found that "Important Features" gave the best 3D model, which fits with the object in the image. The pattern is close to 90% compared with the true size. Apply classification algorithm RF increases the accuracy of the results and reduces computing time for the program. There are many methods for data classifying. One of them is the method of the support vector machine (SVM). The SVM method is represented by Vladimir N. Vapnik (1995) in Support Vector Machines (SVM) - a set of learning algorithms similar with the supervisor has two main tasks: the classification and the regression analysis. In this article we use the method of the SVM classification problem for the size of the human body with 5 classes to compare the performance between SVM methods and Random Forest algorithm. </p>
SLDeep: Statement-Level Software Defect Prediction Using Deep-Learning Models on Static Code Features
<p>Software defect prediction (SDP) seeks to estimate fault-prone areas of the code to focus testing activities on more suspicious portions. Consequently, high-quality software is released with less time and effort. The current SDP techniques however work at coarse-grained units, such as a module or a class, putting some burden on the developers to locate the fault. To address this issue, we propose Statement-Level software defect prediction using Deep-learning model (SLDeep). To reify our proposal, we defined a suite of 32 statement-level metrics, such as the number of binary and unary operators used in a statement. Then, we applied as learning model, long short-term memory (LSTM). The significance of SLDeep for intelligent and expert systems is that it demonstrates a novel use of deep-learning models to the solution of a practical problem faced by software developers. We conducted experiments using more than 100,000 C/C++ programs within the Code4Bench. The programs total 2,356,458 lines of code with 292,064 faulty lines. The benchmark comprises diverse set of programs and versions, written by thousands of developers. Therefore, it tends to give a model that can be used for cross-project SDP. In the experiments, our trained model could successfully classify the unseen data with average performance measures 0.945, 0.971, and 0.976 in terms of recall, precision, and accuracy, respectively. These experimental results suggest that SLDeep is effective for statement-level SDP. The impact of this work is twofold. Working at statement-level further alleviates developer’s burden in pinpointing the fault locations. Second, cross-project feature of SLDeep helps defect prediction research become more industrially-viable</p> <p>for more information visit <a href="https://github.com/sldeep/SLDeep">https://github.com/sldeep/SLDeep</a></p>
Semi-Supervised Pre-trained Foundation Model for 3D Structural Feature Analysis of Seismic Images
<p>Codes, trained model, and datasets for the paper "Semi-Supervised Pre-trained Foundation Model for 3D Structural Feature Analysis of Seismic Images".</p>
Defining Categorical Reasoning of Numerical Feature Models with Feature-Wise and Variant-Wise Quality Attributes
<p><strong>To watch it in Youtube:</strong></p> <p><a href="https://youtu.be/Uq2qtb4_K2U">https://youtu.be/Uq2qtb4_K2U</a></p> <p><strong>This is a pre-print, please access and cite the published version:</strong></p> <p><a href="https://doi.org/10.1145/3503229.3547057">https://doi.org/10.1145/3503229.3547057</a></p> <p>Automatic analysis of variability is an important stage of <em>Software Product Line</em> (SPL) engineering. Incorporating quality information into this stage poses a significant challenge. However, quality-aware automated analysis tools are rare, mainly because in existing solutions variability and quality information are not unified under the same model.</p> <p>In this paper, we make use of the <em>Quality Variability Model</em> (QVM), based on <em>Category Theory</em> (CT), to redefine reasoning operations. We start defining and composing the six most common operations in SPL, but now as quality-based queries, which tend to be unavailable in other approaches. Consequently, QVM supports interactions between variant-wise and feature-wise quality attributes. As a proof of concept, we present, implement and execute the operations as lambda reasoning for CQL IDE -- the state-of-the-art CT tool.</p>
A human genome editing-based MLL-AF4 acute lymphoblastic leukemia model recapitulates key cellular and molecular leukemogenic features. (Processed data)
<p>The prognosis of infant B-cell acute lymphoblastic leukemia (iB-ALL) remains dismal, especially in patients harboring the MLL-AF4 (KTM2A-AFF1) rearrangement, which arises prenatally in early hematopoietic stem/progenitor cells (HSPCs) and accounts for 80% of iB-ALL and 10% of non-infant cases. MLL-AF4+ B-ALL shows a bimodal localization of the MLL gene breakpoint within the MLL break cluster region, and two subgroups of patients based on the gene expression pattern of the HOXA/MEIS cluster have been identified. The pathogenic mechanisms in MLL- AF4+ B-ALL are challenging to study functionally due to the absence of faithful human cellular models recapitulating the disease phenotype and latency. Here, we assess the molecular contribution and leukemogenic capacity of MLL breakpoints occurring in either intron 10 (MLL i10 , centromeric) or intron 12 (MLL i12 , telomeric) in ontogenically-different human HSPCs sourced prenatally (fetal liver) and neonatally (cord blood). CRISPR-Cas9-induced MLL-AF4 (MA) targeting either MLL i10 (M i10 A) or MLL i12 (M i12 A) causes MA-driven in vitro myeloid immortalization in both fetal liver- and cord blood-CD34+ HSPCs. The centromeric location of the MLL breakpoint, but not the cellular ontogeny, determined the expression of HOXA/MEIS1 genes in MLL-edited cells. Centromeric MLL breakpoints endowed enhanced myeloid clonogenic replating to MLL- edited CD34+ HSPCs. The cellular ontogeny and the location of the MLL breakpoint also influenced the capacity of MLL-edited CD34+ HSPCs to initiate pro-B-ALL in vivo, which faithfully recapitulated the molecular, transcriptomic and methylome profiles of patients with primary MA+ iB-ALL. Our data provide key insights into the cellular and molecular leukemogenic determinants of MA+ iB-ALL. This dataset contains processed RNAseq and DNA methylation data from the abovementioned study.</p>
Feature Model for Scrum Adaptation - Evaluation Survey
<p>Feature Model for Scrum Adaptation - Evaluation Survey</p>
Scrum roles adaptations survey and feature model evaluation
<p>Scrum roles adaptations survey and feature model evaluation</p>
Adaptations of Scrum roles in software projects: Survey and Representation Tentative with Feature Models
<p>Vídeo short paper sobre adapatações dos papéis do Scrum</p>
VIBeS Case Studies: Featured Transition Systems and Feature Models
<p>Featured Transition Systems and Feature Models used in the different evaluations presented in: <a href="https://researchportal.unamur.be/en/studentTheses/behavioural-model-based-testing-of-software-product-lines">Behavioural model-based testing of software product lines Devroey, X. (Author). 30 Aug 2017</a></p>
ScienceDex guides
Understand access before you commit
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.