Interaction with the Ethereum Blockchain

App prototyping library for Ethereum-based chains

Abstraction to logical/soft delete in django models

FNF mod which encompasses an LGBT character

Abstract Django models for soft removal

Soft delete models, managers, queryset for Django

Soft-Collect

Persistent storage, returning nil on failure

Dark charcoal theme with soft colors

Python webauthn software authenticator

Emacs24 theme with a light background

Emacs 24 theme with a light background

Soft clustering of time series g... (see metadata)

Soft Maximin Estimation for Larg... (see metadata)

Soft Maximin Estimation for Larg... (see metadata)

Hard and Soft Cluster Validity Indices

Generate trajectories for soft robots from a file

Implements weak, soft, and strong references in Ruby

Soft Windowing on Linear Regression

Density Estimation with Soft Inf... (see metadata)

Phylogenetic Assembly with Soft ... (see metadata)

Soft Classification Performance Measures

Soft Clustering Algorithms

A software synthesizer plugin for the DSSI Soft Synth Interface

Extensible Simulation Package for Research on Soft matter

A typeface with widened proportions, open aperture and soft rounded features

A serif typeface with soft shapes and terminal forms shaped like curls

onscreen soft keyboard for X11

A software synthesizer plugin for the DSSI Soft Synth Interface

Convenient soft-deletion support for Django models

TABS (hard or soft) for indentation (leading whitespace), and SPACES for alignment

DSSI Soft Synth Interface

Modernized DOSBox soft-fork

PyTorch implementations of KMeans, Soft-KMeans and Constrained-KMeans which can be run on GPU and work on (mini-)batches of data.

Asterisk application providing a V.21/V.22/V.23 compatible soft-modem for inbound connections

Soft delete for MongoEngine

Python implementation of several methods for ensembling boxes from object detection models: Non-maximum Suppression (NMS), Soft-NMS, Non-maximum weighted (NMW), Weighted boxes fusion (WBF)

Dark low contrast theme with soft and muted colors

The pymetamodels package combines machine learning (ML) metamodeling and analysis tools for the virtual development of modeling systems within a common abstract framework implemented in an accessible and distributable Python package. This package is oriented to support ML applications in material science, material informatics and the construction of materials, components and systems soft metamodels informed by hard physics-based modelling and experimental characterisations.