This dataset is a Verilog-a implementation of a dynamic compact model of ferroelectric capacitance. It can be run with a SPICE-type circuit simulator. 

Researchers using this dataset should cite it as follows: Ning Feng, Hao Li, Chang Su, Lining Zhang, Qianqian Huang,Runsheng Wang, and Ru Huang,  A Dynamic Compact Model for Ferroelectric Capacitance,Techrxiv, doi: 10.36227/techrxiv.17061260 

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The data collection of the human subjects' brainwaves was performed using a specific experiment of showing a set of pictures that stimulate different emotions on the human subjects. In order to create this kind of experiment, a Java application is developed by us to simulate the visual experiment with the IAPS. It is a simple Java application developed in Eclipse IDE. The main purpose of the application is to collect subjects' credential information and show in sequence the selected set of images from IAPS.

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We have long known that the characterization of protein three-dimensional structure is key to obtaining a detailed understanding of protein function. Computational approaches to protein structure characterization have largely addressed a narrow formulation of the problem, where the goal is the determination of one structure, also known as the native structure, from a given protein amino-acid sequence. However, many researchers over the years have argued for broadening our view of proteins to account for the multiplicity of native structures.

Instructions: 

The .zip file contains 3 folders when unzipped. We provide the details of each folder below.

 

“monomorphic_benchmark_targets” folder: Contains 20 protein targets organized into 20 subfolders. Data for each protein is provided in a subfolder named with its pdb id. Each such subfolder contains the following 4 files.

  1. A .fasta file containing the amino-acid sequence of the protein.

  2. A .pdb file containing the native tertiary conformation coordinates. Detailed format for a .pdb file can be found in http://www.wwpdb.org/documentation/file-format

  3. A .frag3 file containing the fragments of length 3 for the protein sequence generated from http://old.robetta.org/

  4. A .frag9 file containing the fragments of length 9 for the protein sequence generated from http://old.robetta.org/

 

“monomorphic_casp_targets” folder: Contains 10 protein targets organized into 10 subfolders. Data for each protein is provided in a subfolder named with its casp id. Each such subfolder contains the following 4 files.

  1. A .fasta file containing the amino-acid sequence of the protein.

  2. A .pdb file containing the native tertiary conformation coordinates.

  3. A .frag3 file containing the fragments of length 3 for the protein sequence generated from http://old.robetta.org/

  4. A .frag9 file containing the fragments of length 9 for the protein sequence generated from http://old.robetta.org/

 

“metamorphic_benchmark_targets” folder: Contains 18 pairs of protein targets organized into 18 subfolders. Data for each target pair is provided in a subfolder named with its pair id (as indicated in the paper). Each such subfolder contains the following 5 files.

  1. A .fasta file containing the amino-acid sequence common to the pair of target proteins.

  2. A .pdb file containing the native tertiary conformation coordinates for the first target in the target pair.

  3. A .pdb file containing the native tertiary conformation coordinates for the second target in the target pair.

  4. A .frag3 file containing the fragments of length 3 for the protein sequence generated from http://old.robetta.org/

  5. A .frag9 file containing the fragments of length 9 for the protein sequence generated from http://old.robetta.org/

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NLOS/LOS acoustic signal dataset, including 4 rooms, 12,800 data, labeled as NLOS and LOS.

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This dataset was provided by the Turkish company Yapi Kredi Teknoloji and contains a set of 62,380 transactions, of which 61,572 (98.70%) are genuine, and 808 (1.30%) are fraudulent. This imbalanced dataset has 26 variables: the transactions timestamps, the class label (1 for fraud and 0 otherwise), and 24 additional explaining variables. This dataset was also made available in two parts, being one of them for training/testing, with 31,190 transactions (30,400 genuine and 790 fraudulent) and the other, containing 31,190 observations (31,172 genuine e 18 fraudulent) for validation.

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This dataset contains information about actual credit card transactions that occurred in Europe in September of 2013. This dataset is unbalanced, with a total of 284,807 transactions, in which 284,315 (99.83%) are genuine, and 492 (0.17%) are fraudulent. It has a total of 31 variables: the transactions timestamps, the class label (1 for fraud and 0 otherwise), and 29 additional explaining variables.

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The dataset provides textures generated from elliptical cosine and sinc fractional Brownian field models.

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This dataset is composed by both real and sythetic images of power transmission lines, which can be fed to deep neural networks training and applied to line's inspection task. The images are divided into three distinct classes, representing power lines with different geometric properties. The real world acquired images were labeled as "circuito_real" (real circuit), while the synthetic ones were identified as "circuito_simples" (simple circuit) or "circuito_duplo" (double circuit). There are 290 total images for each class, 232 inteded for training and 58 aimed for validation/testing.

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The code and dataset for multimodal unknown surface material classification.

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