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We represented a new Bangla dataset with a Hybrid Recurrent Neural Network model which generated Bangla natural language description of images. This dataset achieved by a large number of images with classification and containing natural language process of images. We conducted experiments on our self-made Bangla Natural Language Image to Text (BNLIT) dataset. Our dataset contained 8,743 images. We made this dataset using Bangladesh perspective images. We used one annotation for each image. In our repository, we added two types of pre-processed data which is 224 × 224 and 500 × 375 respectively alongside annotations of full dataset. We also added CNN features file of whole dataset in our repository which is features.pkl.
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Energy-Efficient Hybrid Flowshop Scheduling
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This data set contains subject responses for a series of perceptual tasks (dot counting and angle identification), as well as the R code used to analyze them. Please see the included README files for details.
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Preprocessed EEG data, behavioral measures, participants infos and experiment code for the study 'An ecological measure of rapid and automatic face-sex categorization'. Please find additional information in 'READ_ME.txt' file.
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The raw dataset obtained during performance tests of the proposed surrogate-based global optimization algorithm (KASRA) on all testing problems mentioned in the paper is included and stored in some zipped files. The dataset is relevant to the illustrative examples and Tables 1−4 in sections 5 and 6 of the paper. Besides, all MATLAB plots in fig-format are presented.
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This data set contains experimental data on characterization of the nature-inspired geometry for fluid flow through a solid porous regenerator used in active magentocaloric cooling applications. The data is sorted into several groups: 1. Passive data. This data was collected during the passive testing of 6 regenerators, of which 3 were made out of aluminum and 3 out of magnetocaloric material (MCM). The aim of these experiments was to determine the thermal effectiveness of the regenerators and the pressure drop though them. Since the aluminum regenerators had better surface quality and higher permeability than MCM ones, passive tests allowed to compare performance of different flow channel geometries. The passive testing on MCM regenerators was done in order to determine pressure drop and qualitatively evaluate surface roughness of the flow channels. 2. Active data. In this data set, the raw data from active magnetic testing are stored. MCM regenerators were characterized in a small-scale magnetocaloric demonstrator. The aim of this study was to investigate the behavior of the MCM regenerators under thermo-magnetic cycling. Each of the data files in this group contains full experimental data, but only the last 200s from the recorded data is used for the analysis, since the regenerators reached the periodic steady state condition. 3. Adiabatic temperature change measurements. This experiment was done under heating protocol. The sample and the cabinet were cooled to 273 K. The sample was thermally insulated from surroundings. Once the experiment was started, the cabinet temperature was increased to 302 K. The sample was cyclically magnetized and demagnetized until it reached thermal equilibrium with the cabinet. The data set contains recordings of sample temperature when it was magnetized and demagnetized, as well as induction effect, which must be eliminated before data analysis. 4. Microscopy images. There are microscopy images of randomly selected flow channels of each regenerator. 5. VSM and DSC data. These data are available only in Matlab figure format.
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Basic data
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3x2x2 design factor 1: condition (feature-alone color, feature-alone shape, binding) factor 2: block (two experimental runs/condition/participants) factor 3: group (old vs young participants) In this study, we compared the electrophysiological activity associated with the binding process in young (N=22, mean age=28.5) and old (N=22; mean age=67.4) participants in a change detection task. Analysis of event-related brain potentials (ERP) focused on the differences between feature-alone (color or shape) and feature-conjunction (color-shape) conditions in stimulus encoding. Independently of aging, discrimination ability was significantly attenuated in the binding condition. The effect, however, was more pronounced in old participants. ERP components related to the visual feature detection and processing (posterior N1 and frontal P2) were not modulated in the binding condition. Only in old participants, a late positive ERP component (LPC) was increased signaling the allocation of additional resources.
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Primary analysis files for bioRxiv manuscript with id 2019/859603 (https://www.biorxiv.org/content/10.1101/859603v1) to evaluate how common variant effect prediction methods capture effect determined by deep mutational scanning experiments. 'data' contains the deep mutational scanning data in a parsed format. See the manuscript for the original data sources which would then be processed with parseRawDatasets.py, followed by manual sequence mapping (resulting in the mapped_seqs.txt files) and then be processed with parseScores.py to result in the .npz files. 'predictionData' contains predictions from SIFT, PolyPhen-2, SNAP2 and Envision, parsed into .npz files. Additional folders are for dummy methods and while executing the below scripts. 'analysis' will contain most of the output files. See below for sample calls to reproduce e.g. Figure 1 from the paper. The scripts are written in Python3 and require, among others, numpy, pandas, scipy, sklearn, rpy2, svgutils and matplotlib. For all scripts the --normalization-scheme flag describes how the experimental scores are processed to fit on the same scale of values. The scheme used for the final manuscript is 'wt0_del_scaled' for deleterious effect variants and 'wt0_ben_scaled' for beneficial effect variants. For compareBinaryDMSToPredictions.py the --binarization-scheme flag describes how scores are binarized to neutral/effect. Possible values are the schemes outlined in the manuscript 'syn90', 'syn95' and 'syn99'.
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