### Multiple Ideal Points: Revealed Preferences in Different Domains

Scott Moser, Abel Rodriguez & Chelsea Lofland
Reproduces all the figures in the paper. Results for the 111th US House of Representatives (Figures 4 to 8) are all reproduced from scratch. Results for any other House between the 97th to the 114th can be reproduced by changing the value of the variable "chouse" in the file "h111.R" to the appropriate number. Summary results for the 97-114th Houses (Figures 1 to 3) are reproduced from summary tables.

### Generalized Rational Variable Projection with Application in ECG Compression

Péter Kovács, Sándor Fridli & Ferenc Schipp
In this paper we develop an adaptive transform-domain technique based on rational function systems. It is of general importance in several areas of signal theory, including filter design, transfer function approximation, system identification, control theory etc. The construction of the proposed method is discussed in the framework of a general mathematical model called variable projection. First we generalize this method by adding dimension type free parameters. Then we deal with the optimization problem of the...

### How often do leading biomedical journals use statistical experts to evaluate statistical methods? The results of a survey.

Tom Hardwicke & Steven Goodman
Scientific claims in biomedical research are typically derived from statistical analyses. However, misuse and misunderstanding of statistical procedures and results permeates the biomedical literature, affecting the validity of those claims. One approach journals have taken to address this issue is to enlist expert statistical reviewers. How many journals do this, how statistical review is incorporated, and how its value is perceived by editors is of interest. Here we report an expanded version of a survey...

### Stochastic Verification of a Game of Tag

Sofie Haesaert & Sadegh Soudjani
Discrete-time stochastic systems are an essential modeling tool for many engineering systems. Building on approximate abstractions, this capsule has methods to compute control strategies with lower bounds for satisfying unbounded temporal logic specifications. Based on a stochastic model of two cars following each other, a driving strategy is developed that ensures a temporal logic specification.

### Equations listed in the appendix of "Non-Reciprocal, robust surface plasmon polaritons on gyrotropic interfaces"

Alexander M. Holmes & Samaneh Pakniyat
This Matlab code is intended to help those who wish to reproduce and build off of results reported in the publication, S. Pakniyat et al., "Non-Reciprocal, robust surface plasmon polaritons on gyrotropic interfaces," in IEEE Transactions on Antennas and Propagation.

### CINSARC prognosis on TCGA samples

Tom Lesluyes & Frédéric Chibon
R methods to: 1) Download both clinical annotations and expression data for sarcomas with complex genetics using TCGAbiolinks. 2) Compute CINSARC prognosis using these information.

### Non-Intrusive Detection of Adversarial Deep Learning Attacks via Observer Networks

Kirthi Shankar Sivamani, Rajeev Sahay & Aly El Gamal
The code provided accompanies the research presented in the IEEE signal processing letter: Non-Intrusive Detection of Adversarial Deep Learning Attacks via Observer Networks. This repository presents a method of detecting adversarial inputs to deep learning models by employing observer networks to classify the outputs of the hidden layers as clean or adversarial. We then take an ensemble of the observer networks to present the final detection prediction. We use two datasets across all experiments: MNIST...

### Eigenvalue-Eigenvector Identity

H. U. Voss
This is supplementary code for the publication H.U. Voss & D.J. Ballon, Recovery of eigenvectors from eigenvalues in systems of coupled harmonic oscillators, Physical Review Research, in press.

### Fitness Dependent Optimizer FDO -Matlab

Jaza Mahmood Abdullah
This code is a Matlab implementation of novel swarm intelligent algorithm , known as the fitness dependent optimizer (FDO). The bee swarming reproductive process and their collective decision-making have inspired this algorithm; it has no algorithmic connection with the honey bee algorithm or the artificial bee colony algorithm.

### MTEX2Gmsh: a tool for generating 2D meshes from EBSD data

Dorian Depriester & Régis Kubler
MTEX2Gmsh is a toolbox for MATLAB. It is designed for generating 2D meshes from EBSD data, in order to investigate the thermomechanical behaviour of polycrystals at grain scale thanks to Finite Element Methods. This toolbox basically converts grain-related data (in the MTEX context) into Gmsh-readable files for meshing; hence, the name.

### MATLAB Code and Data for: Ultra-Scalable Spectral Clustering and Ensemble Clustering (IEEE TKDE 2020)

Dong Huang, Chang-Dong Wang, Jian-Sheng Wu, Jian-Huang Lai & Chee-Keong Kwoh
This repository provides the Matlab source code for two large-scale clustering algorithms, namely, Ultra-Scalable Spectral Clustering (U-SPEC) and Ultra-Scalable Ensemble Clustering (U-SENC), both of which have nearly linear time and space complexity and are capable of robustly and efficiently partitioning ten-million-level nonlinearly-separable datasets on a PC with 64GB memory.

### VNFLOW MATLAB analysis

Valery Vishnevskiy, Jonas Walheim & Sebastian Kozerke
This capsule provides the code for analyzing reconstructed images and creating figures. results/prospective/figs/*.eps: Figure 5 from the manuscipt (prospective undersampling analysis) results/volunteer/figs/*.eps: Figure 3 from the manuscipt (retorspective undersampling) results/volunteer/table.txt: Table 1 from the manuscipt (retorspective undersampling for various acceleration rates)

### Poroviscoelastic Tidal Heating

Yang Liao
This directory has codes for realizing the analytical solutions for a tidal loading problem. The quantitative details are shown in manuscript 'Heating partition between fluid and rock in the core of Enceladus during tidal flexing' by Yang Liao, Francis Nimmo and Jerome Neufeld.

### VNFLOW reconstruction

Valery Vishnevskiy, Jonas Walheim & Sebastian Kozerke
This capsule executes image reconstruction with neural reconstruction models (HamVN and FlowVN). Retrospective and prospective undersampled reconstructions will be stored in the results folder: results/prospective/recon: prospective CS reconstructions results/volunteer/recon: retrospective CS reconstructions for various acceleration factors

### MARCH C- memory testing algorithm using MATLAB

ADITYA KUMAR SINGH PUNDIR
The base algorithm is cited from: [1] A. D. J. V. A. N. D. E. Goor and Y. ZORIAN, “Effective March Algorithms for Testing Single-Order Addressed Memories,” J. Electron. Test. Theory Appl., vol. 345, no. I, pp. 337–345, 1994.

### Repair Rate Calculation of different RA algorithms

ADITYA KUMAR SINGH PUNDIR
Repair Rate Calculation of different RA algorithms the formulas other than hybrid RA algorithms are cited from: [1] W. Jeong, I. Kang, K. Jin, and S. Kang, “A Fast Built-in Redundancy Analysis for Memories With Optimal Repair Rate Using a Line-Based Search Tree,” IEEE Trans. VERY LARGE SCALE Integr. Syst., vol. 17, no. 12, pp. 1665–1678, 2009.

### Memory Built in Self Test and Repair (MBISTR) Ver 1.0

ADITYA KUMAR SINGH PUNDIR
MBISTR Ver. 1.0 Simulation Code

### Comparison between different RA algorithms and proposed hybrid RA algorithms

ADITYA KUMAR SINGH PUNDIR
Comparison between different RA algorithms and proposed hybrid RA algorithms The other area equations are cited from paper below: [1] W. Jeong, I. Kang, K. Jin, and S. Kang, “A Fast Built-in Redundancy Analysis for Memories With Optimal Repair Rate Using a Line-Based Search Tree,” IEEE Trans. VERY LARGE SCALE Integr. Syst., vol. 17, no. 12, pp. 1665–1678, 2009.

### Channel Modeling for Underwater Acoustic Network Simulation

Nils Morozs
This compute capsule contains the code to accompany the following manuscript: N. Morozs, W. Gorma, B. Henson, L. Shen, P. D. Mitchell and Y. V. Zakharov, "Channel Modeling for Underwater Acoustic Network Simulation," submitted to IEEE Communications Surveys & Tutorials, Feb 2020.

### DIAPHANE: a portable radiation transport library for astrophysical applications

Darren S. Reed, Tim Dykes, Ruben Cabezon, Claudio Gheller & Lucio Mayer
DIAPHANE is a portable, scalable, and extensible library for modeling the transport of energy by radiation or relativistic particles. Energy transport modelling is crucial for the hydrodynamic modelling of a wide range of astrophysical phenomena such as planet and galaxy formation, supernova explosions, and cosmic structure evolution. The diaphane library provides a computational framework and functionality to incorporate energy transport modeling into hydrodynamical astrophysics simulations. The initial release comprises a particle-based implementation of Flux Limited...

### PIV Image generator

Luís Mendes, Alexandre Bernardino & Rui Ferreira
NOTE: The code in https://git.qoto.org/CoreRasurae/piv-image-generator could not be imported via git with CodeOcean and was manually inserted, after which a small change was made in exampleAllTestImagesMain to generate an example for each supported flow type. A generator of synthetic images of tracers in turbulent flows is described. Its main application is the benchmarking of Particle Image Velocimetry and Optical Flow algorithms. It can generate tracer images for different types of flows, namely: uniform; parabolic; flow...

### Synchronized Measurement Technology Supported Online Generator Slow Coherency Identification and Adaptive Tracking

Matija Naglic
In an electric power system, slow coherency can be applied to identify groups of the generating units, the rotors of which are swinging together against each other at approximately the same oscillatory frequencies of inter-area modes. This serves as a prerequisite-step of several emergency control schemes to identify power system control areas and improve transient stability. In this capsule, slow coherent generators are grouped based on the direction and the strength of electromechanical coupling between...

### CSOmap v1.0

Xianwen Ren, Guojie Zhong, Qiming Zhang, Lei Zhang, Yujie Sun & Zemin Zhang
This package is for our paper

### Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection

Flavio Martinelli, Giorgia Dellaferrera, Pablo Mainar & Milos Cernak
Inference and model example of spiking network performing Voice Activity Detection with very low spiking activity. More details can be found in accepted ICASSP 2020 publication: https://arxiv.org/abs/1910.09993

### VEnCode package examples

André Macedo & Alisson M. Gontijo
Example notebook on the basic use of the VEnCode package. This package's methods are described in Macedo and Gontijo, 2019, DOI:10.1101/552984. The project can be forked at https://github.com/AndreMacedo88/VEnCode

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