Super Smash Bros. Brawl Tournament
November 21st at 6:00pm Dunn Hall #147
Details
- Entry into the tournament can be made until 6:30pm.
- The entry fee is $5.00.
- First place will be rewarded with 10% of the purse. 50 entries = $25
- Second place will be rewarded with 5% of the purse.
- Garibaldi's Pizza and Drinks will be provided to the particiapnts
- Must be a current UM student to play. Bring your student ID!
Rules and Settings
- Brackets: Double Elimination
- The winner of a Round is the Player who's character is shown as the winner on the end screen.
- 3 stock.
- 8 minutes.
- Items: None.
- Pause: OFF
- Stages: All "First Round" Stages toggled: ON; all others toggled: OFF.
All proceeds from the tournament will go towards supporting the ACM.
Authored by Andrew Hood on November 3rd 2013, last edited November 4th 2013
Events:
ACM Meeting Nov 6th 3:30pm DH 147
ACM Meeting Nov 20th 3:30pm DH 147
Super Smash Bros. Brawl Tournament Nov 21st 6:00pm DH 147
FNC Intership Program Feb 28th 4:00pm TBA


This page tracks the new paper links made to our list of SIGGRAPH 2016 papers.
Page maintained by Ke-Sen Huang.
If you have additions or changes, send an e-mail.
January 22, 2017
- Procedural Voronoi Foams for Additive Manufacturing
- An Anatomically Constrained Local Deformation Model for Monocular Face Capture
August 1, 2016
- Practical Multispectral Lighting Reproduction
- Integration with Stochastic Point Processes
- A Compiler for 3D Machine Knitting
- Perceptual Effect of Shoulder Motions on Crowd Animations
- Mapping Virtual and Physical Reality
- Seamless Visual Sharing with Color-Vision Deficiencies
- Computational Design of Stable Planar-Rod Structures
- Designing Structurally Sound Ornamental Curve Networks
- Printing Arbitrary Meshes With a 5DOF Wireframe Printer
- Motion Graphs for Unstructured Textured Meshes
- Fusion4D: Real-time Performance Capture of Challenging Scenes
- Lightweight Eye Capture Using a Parametric Model
- EyeOpener: Editing Eyes in the Wild
- ZoeMatrope: A System for Physical Material Design
July 31, 2016
- Interactive Sketching of Urban Procedural Models
July 27, 2016
- Body Talk: Crowdshaping Realistic 3D Avatars with Words
July 22, 2016
- Real-time Skeletal Skinning with Optimized Centers of Rotation
July 20, 2016
July 8, 2016
- Reflectance Modeling by Neural Texture Synthesis
- Computational Imaging With Multi-Camera Time-of-Flight Systems
- Binary-Continuous Image Decomposition for Multi-view Display
- Computational Design of Stable Planar-Rod Structures
- Real-time Facial Animation with Image-based Dynamic Avatars
July 7, 2016
- Flow-Guided Warping for Image-Based Shape Manipulation
June 20, 2016
- Printed Perforated Lampshades for Continuous Projective Images
(TOG Paper)
- Connected Fermat Spirals for Layered Fabrication
June 18, 2016
- Globally Optimal Toon Tracking
June 17, 2016
- StyLit: Illumination-Guided Example-Based Stylization of 3D Renderings
- Synthesis of Filigrees for Digital Fabrication
- AutoHair: Fully Automatic Hair Modeling from A Single Image
- Rigel: Flexible Multi-Rate Image Processing Hardware
June 8, 2016
- Adjoint-Driven Russian Roulette and Splitting in Light-Transport Simulation
June 6, 2016
- On the Convexity and Feasibility of the Bounded Distortion Harmonic Mapping Problem
- Parallel Inverse Kinematics for Multithreaded Architectures
(TOG Paper)
- Efficient and Precise Interactive Hand Tracking through Joint, Continuous Optimization of Pose and Correspondences
May 30, 2016
- A Probabilistic Model for Exteriors of Residential Buildings
- Multi-Scale Rendering of Scratched Materials Using a Structured SV-BRDF Model
- Adaptive Polynomial Rendering
May 29, 2016
- A Non-Parametric Factor Microfacet Model for Isotropic BRDFs
May 23, 2016
- ProxImaL: Efficient Image Optimization using Proximal Algorithms
- A Deep Learning Framework for Character Motion Synthesis and Editing
May 22, 2016
- Flow-Guided Warping for Image-Based Shape Manipulation
- Realtime 3D Eye Gaze Animation Using a Single RGB Camera
- The Diffractive Achromat: Full-Spectrum Computational Imaging With Diffractive Optics
- Occluded Imaging With Time-of-Flight Sensors
(TOG Paper)
- Fast 4D Sheared Filtering for Interactive Rendering of Distribution Effects
(TOG Paper)
- Pushing the Limits of 3D Color Printing: Error Diffusion With Translucent Materials
(TOG Paper)
- 3D Mesh Labeling via Deep Convolutional Neural Networks
(TOG Paper)
- Perceptual Effect of Shoulder Motions on Crowd Animations
- Multi-Scale Rendering of Scratched Materials Using a Structured SV-BRDF Model
- Predicting Appearance From Measured Microgeometry of Metal Surfaces
(TOG Paper)
- High Brightness HDR Projection Using Dynamic Freeform Lensing
(TOG Paper)
- Additive Light-Field Displays: Realization of Augmented Reality With Holographic Optical Elements
- Fairy Lights in Femtoseconds: Aerial and Volumetric Graphics Rendered by a Focused Femtosecond Laser Combined With Computational Holographic Fields
(TOG Paper)
- Resolving Fluid Boundary Layers With Particle-Strength Exchange and Weak Adaptivity
- Efficient Fluid Simulation on the Surface of a Sphere
- Multiphase SPH Simulation for Interactive Fluids and Solids
- Reconciling Elastic and Equilibrium Methods for Static Analysis
(TOG Paper)
- Interactive Design of Developable Surfaces
(TOG Paper)
- Printing Arbitrary Meshes With a 5DOF Wireframe Printer
- Non-Smooth Developable Geometry for Interactively Animating Paper Crumpling
(TOG Paper)
- Bounded Distortion Harmonic Shape Interpolation
- Parallel Inverse Kinematics for Multithreaded Architectures
(TOG Paper)
- The Sketchy Database: Learning to Retrieve Badly Drawn Bunnies
- All-Hex Meshing Using Closed Form-Induced Polycubes
- JALI: An Animator-Centric Viseme Model for Expressive Lip Synchronization
- A Probabilistic Model for Exteriors of Residential Buildings
- Intrinsic Girth Function for Shape Processing
(TOG Paper)
- Modeling Dense Inflorescences
- Soli: Ubiquitous Gesture Sensing With Millimeter Wave Radar
- Automatic Photo Adjustment Using Deep Neural Networks
(TOG Paper)
- EyeOpener: Editing Eyes in the Wild
- Sky is Not the Limit
- Preserving Geometry and Topology for Fluid Flows with Thin Obstacles and Narrow Gaps
May 16, 2016
- Perspective-aware Manipulation of Portrait Photos
- Schrodinger's Smoke
- Body Talk: Crowdshaping Realistic 3D Avatars with Words
May 11, 2016
- Unified Motion Planner for Fishes with Various Swimming Styles
May 7, 2016
- Generalized Non-reflecting Boundaries for Fluid Re-Simulation
- Cinema 3D: Large Scale Automultiscopic Display
- Efficient and Flexible Deformation Representation for Data-Driven Surface Modeling
(TOG Paper)
May 6, 2016
- Time-varying Weathering in Texture Space
May 5, 2016
- Task-based Locomotion
- Terrain-Adaptive Locomotion Skills Using Deep Reinforcement Learning
- Guided Learning of Control Graphs for Physics-Based Characters
(TOG Paper)
May 4, 2016
- Accelerated Quadratic Proxy for Geometric Optimization
- Point Registration via Efficient Convex Relaxation
May 1, 2016
- Image Perforation: Automatically Accelerating Image Pipelines by Intelligently Skipping Samples
(TOG Paper)
April 30, 2016
- A Semi-Implicit Material Point Method for the Continuum Simulation of Granular Materials
- Subdivision Exterior Calculus for Geometry Processing
- Emulating Displays with Continuously Varying Frame Rates
- Synthesis of Filigrees for Digital Fabrication
April 29, 2016
- Example-based Plastic Deformation of Rigid Bodies
- Computational Thermoforming
- Synthesis of Filigrees for Digital Fabrication
April 28, 2016
- Efficient and Precise Interactive Hand Tracking through Joint, Continuous Optimization of Pose and Correspondences
- Synthesis of Filigrees for Digital Fabrication
- Non-Linear Shape Optimization Using Local Subspace Projections
- An Interaction-Aware, Perceptual Model for Non-Linear Elastic Objects
- Binary-Continuous Image Decomposition for Multi-view Display
April 27, 2016
- Volume-encoded UV-maps
- cSculpt: A System for Collaborative Sculpting
- ZoeMatrope: A System for Physical Material Design
- CofiFab: Coarse-to-Fine Fabrication of Large 3D Objects
- Fidelity vs. Simplicity : a Global Approach to Line Drawing Vectorization
- Automatically Scheduling Halide Image Processing Pipelines
- A System for Rapid Exploration of Shader Optimization Choices
- Pose-Space Subspace Dynamics
- Shadow Theatre: Discovering Human Motion from a Sequence of Silhouettes
- HexEx: Robust Hexahedral Mesh Extraction
- Fast Approximations for Boundary Element Based Brittle Fracture Simulation
- Example-based Plastic Deformation of Rigid Bodies
April 26, 2016
- Real-time Rendering on a Power Budget
- Computational Network Design from Functional Specifications
- Multi-Scale Label-Map Extraction for Texture Synthesis
April 25, 2016
- Efficient GPU Rendering of Subdivision Surfaces using Adaptive Quadtrees
- Multi-Scale Label-Map Extraction for Texture Synthesis
- Tactile Mesh Saliency
- Automatic Triage for a Photo Series
- Animated Mesh Approximation With Sphere-Meshes
(TOG Paper)
- Approximate Translational Building Blocks for Image Decomposition and Synthesis
(TOG Paper)
April 24, 2016
- Live Intrinsic Video
- Learning How Objects Function via Co-Analysis of Interactions
- Legible Compact Calligrams
- Connected Fermat Spirals for Layered Fabrication
- PATEX: Exploring Pattern Variations
- RAID: A Relation-Augmented Image Descriptor
- Roto++: Accelerating Professional Rotoscoping using Shape Manifolds
- Non-Linear Shape Optimization Using Local Subspace Projections
April 23, 2016
- Position-Normal Distributions for Efficient Rendering of Specular Microstructure
- Fitting Procedural Yarn Models for Realistic Cloth Rendering
- Real-Time Polygonal-Light Shading with Linearly Transformed Cosines
- Schrodinger's Smoke
- Rich360: Optimized Spherical Representation from Structured Panoramic Camera Arrays
- SketchiMo: Sketch-based Motion Editing for Articulated Characters
- Mesh Arrangements for Solid Geometry
- Entropic Metric Alignment for Correspondence Problems
- Interactive Sketching of Urban Procedural Models
- Surface-Only Liquids
- Preserving Geometry and Topology for Fluid Flows with Thin Obstacles and Narrow Gaps
- Physics-driven Pattern Adjustment for Direct 3D Garment Editing
- Generating Dynamically Feasible Trajectories for Quadrotor Cameras
- PiGraphs: Learning Interaction Snapshots from Observations
- Erosion Thickness on Medial Axes of 3D Shapes
- Fast and Exact Discrete Geodesic Computation Based on Triangle-Oriented Wavefront Propagation
- Bijective Maps from Simplicial Foliations
- Drucker-Prager Elastoplasticity for Sand Animation
- Rig Animation with a Tangible and Modular Input Device
- Beyond Developable: Computational Design and Fabrication with Auxetic Materials
- A System for Rapid Exploration of Shader Optimization Choices
Artist-Directed Dynamics for 2D Animation
- GazeStereo3D: Seamless Disparity Manipulations
- Mapping Virtual and Physical Reality
- CofiFab: Coarse-to-Fine Fabrication of Large 3D Objects
April 21, 2016
- Let there be Color!: Joint End-to-end Learning of Global and Local Image Priors for Automatic Image Colorization with Simultaneous Classification
- Learning to Simplify: Fully Convolutional Networks for Rough Sketch Cleanup
- Efficient Dynamic Skinning with Low-Rank Helper Bone Controllers
April 20, 2016
- ProxImaL: Efficient Image Optimization using Proximal Algorithms
- Perspective-aware Manipulation of Portrait Photos
- Bijective Maps from Simplicial Foliations
- Drucker-Prager Elastoplasticity for Sand Animation
- Spectral Style Transfer for Human Motion between Independent Actions
- Computational Design of Stable Planar-Rod Structures
- GazeStereo3D: Seamless Disparity Manipulations
- Realtime 3D Eye Gaze Animation Using a Single RGB Camera
- Emulating Displays with Continuously Varying Frame Rates
- Cinema 3D: Large Scale Automultiscopic Display
- An Interaction-Aware, Perceptual Model for Non-Linear Elastic Objects
- Real-time Skeletal Skinning with Optimized Centers of Rotation
April 19, 2016
- Wasserstein Barycentric Coordinates: Histogram Regression Using Optimal Transport
- Time-varying Weathering in Texture Space
- Shadow Theatre: Discovering Human Motion from a Sequence of Silhouettes
- Fast and Exact Discrete Geodesic Computation Based on Triangle-Oriented Wavefront Propagation
- Crowd-driven Mid-scale Layout Design
April 17, 2016
- Toward Animating Water with Complex Acoustic Bubbles
- Surface-Only Liquids
- AutoHair: Fully Automatic Hair Modeling from A Single Image
- Real-time Facial Animation with Image-based Dynamic Avatars
- Mesh Arrangements for Solid Geometry
- Computational Design of Reconfigurables
April 16, 2016
- Erosion Thickness on Medial Axes of 3D Shapes
April 15, 2016
- Acoustic Voxels: Computational Optimization of Modular Acoustic Filters
April 11, 2016
- Procedural Voronoi Foams for Additive Manufacturing
April 8, 2016
- Modeling Character Canvases from Cartoon Drawings
(TOG Paper)
- Physics-driven Pattern Adjustment for Direct 3D Garment Editing
- My Text in Your Handwriting
(TOG Paper)
- PiGraphs: Learning Interaction Snapshots from Observations
- SemanticPaint: Interactive 3D Labeling and Learning at your Fingertips
(TOG Paper)
- Discrete Connection and Covariant Derivative for Vector Field Analysis and Design
(TOG Paper)
April 7, 2016
- Generating Dynamically Feasible Trajectories for Quadrotor Cameras
- Construction of Manifolds via Compatible Sparse Representations
(TOG Paper)
April 5, 2016
- Surface-Only Liquids
- Generalized Non-reflecting Boundaries for Fluid Re-Simulation
- Fast Approximations for Boundary Element Based Brittle Fracture Simulation
April 4, 2016
- Fusion4D: Real-time Performance Capture of Challenging Scenes
- Roto++: Accelerating Professional Rotoscoping using Shape Manifolds
April 3, 2016
- Pose-Space Subspace Dynamics
- Perspective-aware Manipulation of Portrait Photos
- Detail-preserving Mesh Unfolding for Non-rigid Shape Retrieval
(TOG Paper)
- Tactile Mesh Saliency
- Entropic Metric Alignment for Correspondence Problems
- Interactive Sketching of Urban Procedural Models
- Acoustic Voxels: Computational Optimization of Modular Acoustic Filters
- Interactive Acoustic Transfer Approximation for Modal Sound
(TOG Paper)
- Fidelity vs. Simplicity : a Global Approach to Line Drawing Vectorization
- Rendering on A Power Budget: A Realtime Power-Optimal Rendering Framework
- Non-Linear Shape Optimization Using Local Subspace Projections
- A Deep Learning Framework for Character Motion Synthesis and Editing
April 2, 2016
- Printed Perforated Lampshades for Continuous Projective Images
(TOG Paper)
- Connected Fermat Spirals for Layered Fabrication
- Mesh Arrangements for Solid Geometry
- Computational Design of Reconfigurables
- Rig Animation with a Tangible and Modular Input Device
- Multiple-Scattering Microfacet BSDFs with the Smith Model
- Position-Normal Distributions for Efficient Rendering of Specular Microstructure
- Multiway K-Clustered Tensor Approximation: Toward High- Performance Photorealistic Data-Driven Rendering
(TOG Paper)
- Q-MAT: Computing Medial Axis Transform Using Quadratic Error Minimization
(TOG Paper)
- Multi-View Intrinsic Images of Outdoors Scenes with an Application to Relighting
(TOG Paper)
- Interactively cutting and constraining nodes in a mesh by augmented matrices,
(TOG Paper)
- Efficient 3D Object Segmentation from Densely Sampled Light Fields with Applications to 3D Reconstruction
(TOG Paper)
- Reconstruction of Personalized 3D Face Rigs from Monocular Video
(TOG Paper)
- Ebb: A DSL for Physical Simulation on CPUs and GPUs
- Simit: A Language for Physical Simulation
(TOG Paper)
- Automatically Scheduling Halide Image Processing Pipelines
- ProxImaL: Efficient Image Optimization using Proximal Algorithms
- Rigel: Flexible Multi-Rate Image Processing Hardware
- CofiFab: Coarse-to-Fine Fabrication of Large 3D Objects
- Beyond Developable: Computational Design and Fabrication with Auxetic Materials
- Crowd-driven Mid-scale Layout Design
- Wasserstein Barycentric Coordinates: Histogram Regression Using Optimal Transport
- Mapping Virtual and Physical Reality
- Learning How Objects Function via Co-Analysis of Interactions
- Legible Compact Calligrams
- Fitting Procedural Yarn Models for Realistic Cloth Rendering
- Matching Micro-Appearance Models to Real Fabrics
(TOG Paper)
- Band-Sifting Decomposition for Image Based Material Editing
(TOG Paper)
- RAID: A Relation-Augmented Image Descripto
- Spectral Style Transfer for Human Motion between Independent Actions
- Computational Network Design from Functional Specifications
- PATEX: Exploring Pattern Variations
- Vector Regression Functions for Texture Compression
(TOG Paper)
kesen.huang@gmail.com


Scottish Health Awards: event puts NHS heroes in the spotlight
Expert who collected defibrillators for patients in Ghana is among winners at glittering ceremony.
by Nicola SmithDEVOTED medic Julius Donkor, who has made a life-saving difference to hundreds of patients, was among the NHS heroes to be honoured at the Scottish Health Awards.
A cardiac physiologist at Aberdeen Royal Infirmary, Julius was the inaugural winner of the Global Citizenship Award for his tireless efforts to collect expired medical equipment to send to hospitals his native Ghana.
The awards, now in their 18th year, took place at Edinburgh Corn Exchange on Thursday and once again shone a spotlight on the individuals and teams who make it their life mission to deliver high-quality health and social care.
Julius, of NHS Grampian, was prompted to take action after visiting family in Ghana and noticing that many hospitals in the country did not have defibrillators. Now he has collected more than 100 of the life-saving devices and delivered them to the African nation.
Julius said: “Defibrillators have made a huge difference in Ghana, they are saving lives.
“My grandmother started this. She is the one who inspired this about five or six years ago when she fell ill in Ghana and my exposure there is what made me want to help in their hospitals.”
Trophies at the annual awards ceremony, which is run by the Daily Record in partnership with NHS Scotland and the Scottish Government, were handed out by Cabinet Secretary for Health and Sport Jeane Freeman.
She said: “These awards represent the very high regard that people in Scotland hold for our health and social care services. They are a testimony to you. Every single day you get up in the morning and go to work determined to do the best possible job that you can.
 You are welcome. And we so want you to stay’.”

She is the only therapy support worker in the community multi-disciplinary team in the child and adolescent mental health practice at the Kirkman Clinic in Kirkcaldy, and it’s thanks to her intervention and support that many of her patients haven’t had to be admitted to an adolescent psychiatric hospital.
The Innovation Award was dedicated to those in the NHS who push boundaries to develop new forms of treatment for their patients.
Picking up the award was the Intelligent Liver Function Testing Team, which brings together experts from NHS Tayside and the University of Dundee whose aim is to reduce the mortality, morbidity and cost of late-presenting liver disease.
The Volunteers Award went to East Renfrewshire Health and Social Care Partnership’s Anne Marie Kennedy MBE, who has volunteered in East Renfrewshire for more than 20 years.
Anne Marie manages the Wee Red Bus, which provides transport and support for people who are isolated, frail, elderly, with mobility needs or have mental health issues to attend medical appointments.
She said: “Thank you to my volunteers because they’ve made the difference, as well as our health and social care partnership because they support us and provide the funds.”
Harvé Symonds went home with the Young Achiever Award.
A patient she cared for as an auxiliary nurse at the Balfour Hospital in Kirkwall in NHS Orkney nominated her after being admitted due to an injury while on holiday on the island.
Dr Scott Mackie of NHS Lanarkshire picked up the Optometrist Award for his work with a patient who had incomplete achromatopsia and type-two cone dystrophy, a rare visual impairment leading to multiple vision problems.
NHS Ayrshire & Arran’s Ann Allan collected the Midwife Award for her work in supporting vulnerable women throughout their pregnancies.
She said: “There are only seven midwives and we cover the whole of Ayrshire, covering all aspects of vulnerability.”
Helen Scott, of NHS Greater Glasgow and Clyde, has spent 36 years as a physiotherapist and is a world expert on amputee rehabilitation, setting up the first amputee support group in Scotland back in 1897. She took home the Therapist Award.
Meanwhile, Jordan Bain from NHS Greater Glasgow and Clyde collected the Dentist Award.
He was born into a home where drugs were prevalent and enjoys his time working in Springburn, claiming it’s a privilege “to make people smile”.
The Care at Home Pharmacy Technician Service Team of NHS Ayrshire & Arran scooped the Integrated Care Award for their work with elderly and vulnerable people with medication issues, and the Care for Mental Health Award was presented to worthy winners National Distress Brief Intervention Programme Team of NHS Lanarkshire, who bring together health and social care with the emergency services to provide early intervention and improve the outcomes of people experiencing distress.
The Healthier Lifestyle Award went to Wendy Handley of NHS Forth Valley for her work with young people in custody and the Leader of the Year Award was presented to Dr Erica Peters of NHS Greater Glasgow and Clyde for her work in tackling the rise of HIV diagnoses among people who inject drugs.
Alison Irvine was named winner in the Nurse category for her dedication to treatment of diabetes across NHS Shetland. It’s down to Alison that there are now insulin pumps on the island.
The Doctor Award was presented to Dr Howard Kennedy of NHS Borders who provides round-the-clock care for his patients, often visiting in the evening and at night.
The theatre reception team of NHS Greater Glasgow and Clyde – who play a key role in keeping children calm before surgery – picked up the Top Team Award and Tony McLaren, NHS 24, collected the Unsung Hero Award on the night.
The awards were hosted once again by comedian Fred MacAulay, who said: “I’d like to extend my congratulations to all the finalists and winners, it’s been a tremendous occasion.”


[gecode-users] Number of solutions
Sebastian Kosch
sebastian.kosch at mail.utoronto.ca
Fri Jan 20 13:46:08 CET 2017
Hm – that being said, chiming in as a casual user – I've wondered before
whether a tex(t) source of the PDF is available.
No question: MPG is one of the best piece of documentation I've ever had
the pleasure of reading. The breadth and depth and coherence really are
an impressive accomplishment.
The underlying expectation, though, is that users read it like a
textbook – when realistically people just want to google their problem,
click, immediately be in the right section, aha, click, there's the link
to the function reference, click, open a new tab, etc. When you work
with a system as large as Gecode for a few days, constantly going back
and forth between scrolling through a massive PDF and looking for
corresponding doxygen pages becomes a bit tedious.
Yes, a total luxury complaint. I also get that you're worried about a
bunch of different versions of the docs floating around. Just maybe if
you publish the source people can experiment with pandoc-ing it straight
into a doxygen or Sphinx format, and perhaps it'll become apparent that
they really find that easier to use?
Sebastian
On 2017-01-19 09:36 AM, Christian Schulte wrote:
> What? That is a real luxury complaint...
>
> You know how much work it is to write a coherent 500 page document? That's
> not that easy to do as html.
>
> And searching in PDF is not that hard...
>
> Christian
>
> --
> Christian Schulte, www.gecode.org/~schulte
> Professor of Computer Science, KTH, cschulte at kth.se
> Expert Researcher, RISE ICT/SICS, cschulte at sics.se
>
>
> -----Original Message-----
> From: users-bounces at gecode.org [mailto:users-bounces at gecode.org] On Behalf
> Of Johannes Waldmann
> Sent: Thursday, January 19, 2017 15:19
> To: users at gecode.org
> Subject: Re: [gecode-users] Number of solutions
>
>> ... you should read MPG, really
>
> Seconded. But - (Downloading and) opening a 500 page PDF is rather heavy. Is
> there a HTML version, on the web somewhere, with URLs for sections, so that
> it's easy to refer to?
>
> I know there is "online HTML" doc at
> http://www.gecode.org/documentation.html
> but this refers to the C++ API only (?)
> E.g., when I search for "command line"
> (expecting to find something towards the answer you gave, "commandline
> -solutions 0 -mode stat"), this is not really helpful:
> http://www.gecode.org/doc-latest/reference/search.php?query=command+line
>
> - J.W.
>
>
> _______________________________________________
> Gecode users mailing list
> users at gecode.org
> https://www.gecode.org/mailman/listinfo/gecode-users
>
>
>
> _______________________________________________
> Gecode users mailing list
> users at gecode.org
> https://www.gecode.org/mailman/listinfo/gecode-users
>
More information about the users
mailing list


Cipher Officer Leopard:
I know that prayer... it's very simple and straightforward. Why do you ask about it?
"I believe it may contain a hidden message..."


Nirdizati Research frontend¶
Master
Development
React frontend to perform Predictive Monitoring analysis over event logs.
Running in a new environment¶
The docker build is available @ https://hub.docker.com/r/nirdizatiresearch/predict-react/ in any case if you prefer to setup your environment on yuor own you can refer the Dockerfile.
Run an instance of the project¶
If you are familiar with docker-compose the docker-compose file is available, otherwise if you use pycharm as IDE is available the run configuration in the runConfiguration settings.
package.json contains all supported commands for this project.
Install required components:
npm install
Run build:
npm run build
Run tests:
npm run test
Run start:
npm run start
Thanks to¶
This project was bootstrapped with Create React App and Storybook.
Contributors¶
- @stebranchi Stefano Branchi
- @dfmchiara Chiara Di Francescomarino
- @TKasekamp Tõnis Kasekamp
- @mrsonuk Santosh Kumar
- @fmmaggi Fabrizio Maggi
- @WilliamsRizzi Williams Rizzi
- @HitLuca Luca Simonetto


{% extends 'movie/base.html' %} {% block title %} detail {% endblock %} {% block static%} {% load static %} {% endblock %} {% block content %}
Released in: {{object.ReleaseYear}}
Director: {{object.Director}}
Language: {{object.Language}}
Cast 1: {{object.Cast_I}}
Cast 2: {{object.Cast_II}}
Imdb Rating: {{object.ImdbRating}}
Genre: {{object.Genre}}


Definition
Average potential evapotranspiration (PE) for wheat (mm)
Method of calculation
Sum of average daily values of Potential evapotranspiration (PE) from GDD2Start to STOPWheat.
If STOPWheat is later than GDD2Stop (estimated average date of first fall frost), then accumulation is stopped on GDD2Stop.
PE is calculated using the Baier-Roberson formulae (Baier and Robertson, 1965; Baier, 1971) by default, or as selected by the user.


Stephen is a full stack developer with an addiction to devops. When he's not writing code or automating things, he plays with his two kids and manages an amatuer baseball team.
If you liked this post, you can share it with your followers or follow me on Twitter!


This partial model provides a conditional heating port for the
connection to a thermal network.
- If useHeatPort is set to
false (default), no heat port is available, and
the thermal loss power flows internally to the ground. In this
case, the parameter T specifies the fixed device
temperature (the default for T = 20oC).
- If useHeatPort is set to
true, a heat port is available.
If this model is used, the loss power has to be provided by an
equation in the model which inherits from ConditionalHeatingPort
model (lossPower = ...). As device temperature
T_heatPort can be used to describe the influence
of the device temperature on the model behaviour.
- February 17, 2009 by Christoph Clauss
initially implemented
Generated at 2019-01-25T16:44:03Z by OpenModelica 1.14.0~dev-130-gefa44fb


Contact Us
- Talk to Us
Chat with us directly via our Discord server.
Drop a message in #general, and add @Organiser to your message. We should get back to you soon!
- Email Us
If you're feeling formal, you can reach us at:
<EMAIL_ADDRESS>- Tweet at Us
We're on Twitter
@quickcade


HackTrin V is the fifth iteration of Trinity School’s annual hackathon. Middle and high schoolers, of any experience level, can participate. You can form teams of 1-4 students, even across different schools, and work to make projects relating to coding. We'll have workshops and mentors to help you with your projects and learn! We will announce the surprise theme at our Wednesday night kickoff event the week of HackTrin, after which you can start coding, and continue until the end of the Sunday, March 4 event. You’ll present your projects to our panels of judges to win prizes in multiple categories (including a beginner category)!
Trinity Team
Jake Fisher '18
President
Matteo de Donato '19
Vice President
Karen Shi '18
Co-Head of Student Outreach Senior Advisor
Calvin Hansen '18
Co-Head of Student Outreach
Claudia Chapman '18
Head of Workshops
Alex Miller '18
Head of Hardware
Will Cramer '18
Head of Marketing and Social Media


ICT, Education, E-Learning
Location: Dublin, Ireland
Admin: Sara Weber
Java Programmer
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Sara updated her status to:
"Really enjoying the Societies Conference today!"
2:24PM
Joe Bloggs presentation on ICT in Education is very interesting and informative!
4:24PM
HP SmartPrint Z200a
Dublin, Ireland - 53°20' 52" N 6°1' 3" W
Sport, ICT, Research
ICT Researcher


| Total net profit | -8.51 | Gross profit | 37.88 | Gross loss | -46.39 |
| Profit factor | 0.82 | Expected payoff | -0.85 | ||
| Absolute drawdown | -13.88 | Maximal drawdown (%) | 46.39 (142.69%) | ||
| Total trades | 10 | Profit trades (% of total) | 3 (30.0%) | Loss trades (% of total) | 7 (70.0%) |
| Long positions (won %) | 6 (33.33%) | Short positions (won %) | 4 (25.0%) | ||
| Largest | profit trade | 32.51 | loss trade | -9.05 | |
| Average | profit trade | 12.63 | loss trade | -6.63 | |
| Minimum | profit trade | 0.11 | loss trade | -1.01 | |
| Maximum | take profit in pips | 233.5 | stop loss in pips | -51.1 | |
| Average | take profit in pips | 43.4 | stop loss in pips | -38.5 | |
| Minimum | take profit in pips | -5.4 | stop loss in pips | -28.9 | |
| Maximum | Reward-Risk | 5.9 | Duration in minutes | 8605 | |
| Average | Reward-Risk | 1.2 | Duration in minutes | 1085.3 | |
| Minimum | Reward-Risk | 0.0 | Duration in minutes | 10 | |
| Maximum | consecutive wins (profit in money) | 2.0 (5.37) | consecutive losses (loss in money) | 7.0 (-46.39) | |
| Average | consecutive wins | 1.0 | consecutive losses | 7.0 |
| Total net profit | 3.72 | Gross profit | 37.77 | Gross loss | -34.05 |
| Profit factor | 1.11 | Expected payoff | 0.62 | ||
| Absolute drawdown | -1.54 | Maximal drawdown (%) | 34.05 (104.74%) | ||
| Total trades | 6 | Profit trades (% of total) | 2 (33.33%) | Loss trades (% of total) | 4 (66.67%) |
| Largest | profit trade | 32.51 | loss trade | -9.05 | |
| Average | profit trade | 18.88 | loss trade | -8.51 | |
| Minimum | profit trade | 5.26 | loss trade | -7.3 | |
| Maximum | take profit in pips | 233.5 | stop loss in pips | -44.4 | |
| Average | take profit in pips | 62.3 | stop loss in pips | -41.0 | |
| Minimum | take profit in pips | 13.7 | stop loss in pips | -35.0 | |
| Maximum | Reward-Risk | 5.9 | Duration in minutes | 8605.0 | |
| Average | Reward-Risk | 1.6 | Duration in minutes | 1693.3 | |
| Minimum | Reward-Risk | 0.3 | Duration in minutes | 29.0 | |
| Maximum | consecutive wins (profit in money) | 1.0 (32.51) | consecutive losses (loss in money) | 4.0 (-34.05) | |
| Average | consecutive wins | 1.0 | consecutive losses | 4.0 |
| Total net profit | -12.23 | Gross profit | 0.11 | Gross loss | -12.34 |
| Profit factor | 0.01 | Expected payoff | -3.06 | ||
| Absolute drawdown | -12.34 | Maximal drawdown (%) | 6.69 (-118.41%) | ||
| Total trades | 4 | Profit trades (% of total) | 1 (25.0%) | Loss trades (% of total) | 3 (75.0%) |
| Largest | profit trade | 0.11 | loss trade | -5.68 | |
| Average | profit trade | 0.11 | loss trade | -4.11 | |
| Minimum | profit trade | 0.11 | loss trade | -1.01 | |
| Maximum | take profit in pips | 51.8 | stop loss in pips | -51.1 | |
| Average | take profit in pips | 14.9 | stop loss in pips | -34.7 | |
| Minimum | take profit in pips | -5.4 | stop loss in pips | -28.9 | |
| Maximum | Reward-Risk | 1.8 | Duration in minutes | 598.0 | |
| Average | Reward-Risk | 0.6 | Duration in minutes | 173.3 | |
| Minimum | Reward-Risk | Duration in minutes | 10.0 | ||
| Maximum | consecutive wins (profit in money) | 1.0 (0.11) | consecutive losses (loss in money) | 3.0 (-12.34) | |
| Average | consecutive wins | 1.0 | consecutive losses | 3.0 |


There appears to be a "Train Wreck" in your code.
${code}
In short, a train wreck is a method call chain that navigates the structure of the program to retrieve data that is needed for a computation.
For example, consider the method call chain customer.getAddress().getZipCode() which asks the customer for its address, only to ask that for its zip code.
The problem is that not only does the current class now know about the customer, it also knows about its address and more specifically about the zip code of this address. This makes the design fragile. Should any of the classes change, this may affect the current class, even though it does not own any address or zip code directly.
Instead, methods should only ever tell their neighbour objects to perform a certain task, they should never ask for data in order to do the task themselves.
${if improvement}


HOLY WELL AT LITTLE CONAN
ON Palm Sunday the people resorted to the well sacred to "Our Lady of Nants," with a cross of palm, and after making the priest a present, they' were allowed to throw the cross into the well; if it swam the thrower was to outlive the year, if sank he was not. [a]
[a] Carew.


biom-format Table objects¶
The biom-format project provides rich Table objects to support use of the BIOM file format. The objects encapsulate matrix data (such as OTU counts) and abstract the interaction away from the programmer. This provides the immediate benefit of the programmer not having to worry about what the underlying data object is, and in turn allows for different data representations to be supported. Currently, biom-format supports a dense object built off of numpy.array (NumPy) and a sparse object built off of Python dictionaries.
biom-format table_factory method¶
Generally, construction of a Table subclass will be through the table_factory method. This method facilitates any necessary data conversions and supports a wide variety of input data types.
- biom.table.table_factory(data, sample_ids, observation_ids, sample_metadata=None, observation_metadata=None, table_id=None, constructor=<class 'biom.table.SparseOTUTable'>, **kwargs)¶
Construct a table
Attempts to make ‘data’ sane with respect to the constructor type through various means of juggling. Data can be:
- numpy.array
- list of numpy.array vectors
- SparseObj representation
- dict representation
- list of SparseObj representation vectors
- list of lists of sparse values [[row, col, value], ...]
- list of lists of dense values [[value, value, ...], ...]
Example usage to create a SparseOTUTable object:
from biom.table import table_factory, SparseOTUTable from numpy import array sample_ids = ['s1','s2','s3','s4'] sample_md = [{'pH':4.2,'country':'Peru'}, {'pH':5.2,'country':'Peru'}, {'pH':5.0,'country':'Peru'}, {'pH':4.9,'country':'Peru'}] observation_ids = ['o1','o2','o3'] observation_md = [{'domain':'Archaea'}, {'domain':'Bacteria'}, {'domain':'Bacteria'}] data = array([[1,2,3,4], [-1,6,7,8], [9,10,11,12]]) t = table_factory(data, sample_ids, observation_ids, sample_md, observation_md, constructor=SparseOTUTable)
Description of available Table objects¶
There are multiple objects available but some of them are unofficial abstract base classes (does not use the abc module for historical reasons). In practice, the objects used should be the derived Tables such as SparseOTUTable or DenseGeneTable.
Abstract base classes¶
Abstract base classes establish standard interfaces for subclassed types and provide common functionality for derived types.
Table¶
Table is a container object and an abstract base class that provides a common and required API for subclassed objects. Through the use of private interfaces, it is possible to create public methods that operate on the underlying datatype without having to implement each method in each subclass. For instance, Table.iterSamplesData will return a generator that always yields numpy.array vectors for each sample regardless of how the table data is actually stored. This functionality results from derived classes implementing private interfaces, such as Table._conv_to_np.
- class biom.table.Table(Data, SampleIds, ObservationIds, SampleMetadata=None, ObservationMetadata=None, TableId=None, **kwargs)¶
Abstract base class representing a Table.
Once created, a Table object is immutable except for its sample/observation metadata, which can be modified in place via addSampleMetadata and addObservationMetadata.
- Code to simulate immutability taken from here:
- http://en.wikipedia.org/wiki/Immutable_object
- addObservationMetadata(md)¶
Take a dict of metadata and add it to an observation.
md should be of the form {observation_id:{dict_of_metadata}}
- addSampleMetadata(md)¶
Take a dict of metadata and add it to a sample.
md should be of the form {sample_id:{dict_of_metadata}}
- binObservationsByMetadata(f, constructor=None)¶
Yields tables by metadata
f is given the observation metadata by row and must return what “bin” the observation is part of.
constructor: the type of binned tables to create, e.g. SparseTaxonTable. If None, the binned tables will be the same type as this table.
- binSamplesByMetadata(f, constructor=None)¶
Yields tables by metadata
f is given the sample metadata by row and must return what “bin” the sample is part of.
constructor: the type of binned tables to create, e.g. SparseTaxonTable. If None, the binned tables will be the same type as this table.
- collapseObservationsByMetadata(metadata_f, reduce_f=<built-in function add>, norm=True, min_group_size=2, include_collapsed_metadata=True, constructor=None, one_to_many=False, one_to_many_mode='add', one_to_many_md_key='Path', strict=False)¶
Collapse observations in a table by observation metadata
Bin observations by metadata then collapse each bin into a single observation.
If include_collapsed_metadata is True, metadata for the collapsed observations are retained and can be referred to by the ObservationId from each observation within the bin.
constructor: the type of collapsed table to create, e.g. SparseTaxonTable. If None, the collapsed table will be the same type as this table.
The remainder is only relevant to setting one_to_many to True.
If one_to_many is True, allow observations to fall into multiple bins if the metadata describe a one-many relationship. Supplied functions must allow for iteration support over the metadata key and must return a tuple of (path, bin) as to describe both the path in the hierarchy represented and the specific bin being collapsed into. The uniqueness of the bin is _not_ based on the path but by the name of the bin.
The metadata value for the corresponding collapsed row may include more (or less) information about the collapsed data. For example, if collapsing “KEGG Pathways”, and there are observations that span three pathways A, B, and C, such that observation 1 spans A and B, observation 2 spans B and C and observation 3 spans A and C, the resulting table will contain three collapsed observations:
- A, containing original observation 1 and 3
- B, containing original observation 1 and 2
- C, containing original observation 2 and 3
If an observation maps to the same bin multiple times, it will be counted multiple times.
There are two supported modes for handling one-to-many relationships via one_to_many_mode: add and divide. add will add the observation counts to each bin that the observation maps to, which may increase the total number of counts in the output table. divide will divide an observation’s counts by the number of metadata that the observation has before adding the counts to each bin. This will not increase the total number of counts in the output table.
If one_to_many_md_key is specified, that becomes the metadata key that describes the collapsed path. If a value is not specified, then it defaults to ‘Path’.
If strict is specified, then all metadata pathways operated on must be indexable by metadata_f.
one_to_many and norm are not supported together.
one_to_many and reduce_f are not supported together.
one_to_many and min_group_size are not supported together.
A final note on space consumption. At present, the one_to_many functionality requires a temporary dense matrix representation. This was done so as it initially seems like true support requires rapid __setitem__ functionality on the SparseObj and at the time of implementation, CSMat was O(N) to the number of nonzero elements. This is a work around until either a better __setitem__ implementation is in play on CSMat or a hybrid solution that allows for multiple SparseObj types is used.
- collapseSamplesByMetadata(metadata_f, reduce_f=<built-in function add>, norm=True, min_group_size=2, include_collapsed_metadata=True, constructor=None, one_to_many=False, one_to_many_mode='add', one_to_many_md_key='Path', strict=False)¶
Collapse samples in a table by sample metadata
Bin samples by metadata then collapse each bin into a single sample.
If include_collapsed_metadata is True, metadata for the collapsed samples are retained and can be referred to by the SampleId from each sample within the bin.
constructor: the type of collapsed table to create, e.g. SparseTaxonTable. If None, the collapsed table will be the same type as this table.
The remainder is only relevant to setting one_to_many to True.
If one_to_many is True, allow samples to collapse into multiple bins if the metadata describe a one-many relationship. Supplied functions must allow for iteration support over the metadata key and must return a tuple of (path, bin) as to describe both the path in the hierarchy represented and the specific bin being collapsed into. The uniqueness of the bin is _not_ based on the path but by the name of the bin.
The metadata value for the corresponding collapsed column may include more (or less) information about the collapsed data. For example, if collapsing “FOO”, and there are samples that span three associations A, B, and C, such that sample 1 spans A and B, sample 2 spans B and C and sample 3 spans A and C, the resulting table will contain three collapsed samples:
- A, containing original sample 1 and 3
- B, containing original sample 1 and 2
- C, containing original sample 2 and 3
If a sample maps to the same bin multiple times, it will be counted multiple times.
There are two supported modes for handling one-to-many relationships via one_to_many_mode: add and divide. add will add the sample counts to each bin that the sample maps to, which may increase the total number of counts in the output table. divide will divide a sample’s counts by the number of metadata that the sample has before adding the counts to each bin. This will not increase the total number of counts in the output table.
If one_to_many_md_key is specified, that becomes the metadata key that describes the collapsed path. If a value is not specified, then it defaults to ‘Path’.
If strict is specified, then all metadata pathways operated on must be indexable by metadata_f.
one_to_many and norm are not supported together.
one_to_many and reduce_f are not supported together.
one_to_many and min_group_size are not supported together.
A final note on space consumption. At present, the one_to_many functionality requires a temporary dense matrix representation. This was done so as it initially seems like true support requires rapid __setitem__ functionality on the SparseObj and at the time of implementation, CSMat was O(N) to the number of nonzero elements. This is a work around until either a better __setitem__ implementation is in play on CSMat or a hybrid solution that allows for multiple SparseObj types is used.
- copy()¶
Returns a copy of the table
- delimitedSelf(delim='t', header_key=None, header_value=None, metadata_formatter=<type 'str'>, observation_column_name='#OTU ID')¶
Return self as a string in a delimited form
Default str output for the Table is just row/col ids and table data without any metadata
Including observation metadata in output: If header_key is not None, the observation metadata with that name will be included in the delimited output. If header_value is also not None, the observation metadata will use the provided header_value as the observation metadata name (i.e., the column header) in the delimited output.
metadata_formatter: a function which takes a metadata entry and returns a formatted version that should be written to file
observation_column_name: the name of the first column in the output table, corresponding to the observation IDs. For example, the default will look something like:
#OTU ID Sample1 Sample2 OTU1 10 2 OTU2 4 8
- descriptiveEquality(other)¶
For use in testing, describe how the tables are not equal
- filterObservations(f, invert=False)¶
Filter observations from self based on f
f must accept three variables, the observation values, observation ID and observation metadata. The function must only return True or False, where True indicates that an observation should be retained.
invert: if invert == True, a return value of True from f indicates that an observation should be discarded
- filterSamples(f, invert=False)¶
Filter samples from self based on f
f must accept three variables, the sample values, sample ID and sample metadata. The function must only return True or False, where True indicates that a sample should be retained.
invert: if invert == True, a return value of True from f indicates that a sample should be discarded
- getBiomFormatJsonString(generated_by, direct_io=None)¶
Returns a JSON string representing the table in BIOM format.
generated_by: a string describing the software used to build the table
If direct_io is not None, the final output is written directly to direct_io during processing.
- getBiomFormatObject(generated_by)¶
Returns a dictionary representing the table in BIOM format.
This dictionary can then be easily converted into a JSON string for serialization.
generated_by: a string describing the software used to build the table
TODO: This method may be very inefficient in terms of memory usage, so it needs to be tested with several large tables to determine if optimizations are necessary or not (i.e. subclassing JSONEncoder, using generators, etc...).
- getBiomFormatPrettyPrint(generated_by)¶
Returns a ‘pretty print’ format of a BIOM file
generated_by: a string describing the software used to build the table
WARNING: This method displays data values in a columnar format and can be misleading.
- getObservationIndex(obs_id)¶
Returns the observation index for observation obs_id
- getSampleIndex(samp_id)¶
Returns the sample index for sample samp_id
- getTableDensity()¶
Defined by subclass
- getValueByIds(obs_id, samp_id)¶
Return value in the matrix corresponding to (obs_id, samp_id)
- isEmpty()¶
Returns True if the table is empty
- iterObservationData()¶
Yields observation values
- iterObservations(conv_to_np=True)¶
Yields (observation_value, observation_id, observation_metadata)
NOTE: will return None in observation_metadata positions if self.ObservationMetadata is set to None
- iterSampleData()¶
Yields sample values
- iterSamples(conv_to_np=True)¶
Yields (sample_value, sample_id, sample_metadata)
NOTE: will return None in sample_metadata positions if self.SampleMetadata is set to None
- merge(other, Sample='union', Observation='union', sample_metadata_f=<function prefer_self at 0x2a6b668>, observation_metadata_f=<function prefer_self at 0x2a6b668>)¶
Merge two tables together
The axes, samples and observations, can be controlled independently. Both can either work on union or intersection.
sample_metadata_f and observation_metadata_f define how to merge metadata between tables. The default is to just keep the metadata associated to self if self has metadata otherwise take metadata from other. These functions are given both metadata dictsand must return a single metadata dict
NOTE: There is an implicit type conversion to float. Tables using strings as the type are not supported. No check is currently in place.
NOTE: The return type is always that of self
- nonzero()¶
Returns locations of nonzero elements within the data matrix
The values returned are (observation_id, sample_id)
- nonzeroCounts(axis, binary=False)¶
Get nonzero summaries about an axis
axis : either ‘sample’, ‘observation’, or ‘whole’ binary : sum of nonzero entries, or summing the values of the entries
Returns a numpy array in index order to the axis
- normObservationByMetadata(obs_metadata_id)¶
Return new table with vals divided by obs_metadata_id
- normObservationBySample()¶
Return new table with vals as relative abundances within each sample
- normSampleByObservation()¶
Return new table with vals as relative abundances within each obs
- observationData(id_)¶
Return samples associated with observation id id_
- observationExists(id_)¶
Returns True if observation id_ exists, False otherwise
- reduce(f, axis)¶
Reduce over axis with f
axis can be either sample or observation
- sampleData(id_)¶
Return observations associated with sample id id_
- sampleExists(id_)¶
Returns True if sample id_ exists, False otherwise
- sortByObservationId(sort_f=<function natsort at 0x2a6b5f0>)¶
Return a table where observations are sorted by sort_f
sort_f must take a single parameter: the list of observation ids
- sortBySampleId(sort_f=<function natsort at 0x2a6b5f0>)¶
Return a table where samples are sorted by sort_f
sort_f must take a single parameter: the list of sample ids
- sortObservationOrder(obs_order)¶
Return a new table with observations in observation order
- sortSampleOrder(sample_order)¶
Return a new table with samples in sample_order
- sum(axis='whole')¶
Returns the sum by axis
axis can be:
whole : whole matrix sum
sample : return a vector with a sum for each sample
observation : return a vector with a sum for each observation
- transformObservations(f)¶
Iterate over observations, applying a function f to each value
f must take three values: an observation value (int or float), an observation id, and an observation metadata entry, and return a single value (int or float) that replaces the provided observation value
- transformSamples(f)¶
Iterate over samples, applying a function f to each value
f must take three values: a sample value (int or float), a sample id, and a sample metadata entry, and return a single value (int or float) that replaces the provided sample value
- transpose()¶
Return a new table that is the transpose of this table.
The returned table will be an entirely new table, including copies of the (transposed) data, sample/observation IDs and metadata.
OTUTable¶
The OTUTable base class provides functionality specific for OTU tables. Currently, it only provides a static private member variable that describes its BIOM type. This object was stubbed out incase future methods are developed that do not make sense with the context of, say, an MG-RAST metagenomic abundance table. It is advised to always use an object that subclasses OTUTable if the analysis is on OTU data.
- class biom.table.OTUTable¶
OTU table abstract class
PathwayTable¶
A table type to represent gene pathways.
- class biom.table.PathwayTable¶
Pathway table abstract class
FunctionTable¶
A table type to represent gene functions.
- class biom.table.FunctionTable¶
Function table abstract class
OrthologTable¶
A table type to represent gene orthologs.
- class biom.table.OrthologTable¶
Ortholog table abstract class
Container classes¶
The container classes implement required private member variable interfaces as defined by the Table abstract base class. Specifically, these objects define the ways in which data is moved into and out of the contained data object. These are fully functional and usable objects, however they do not implement table type specifc functionality.
SparseTable¶
The subclass SparseTable can be derived for use with table data. This object implemented all of the required private interfaces specified by the Table base class. The object contains a _data private member variable that is an instance of the current sparse backend. It is advised to used derived objects of SparseTable if the data being operated on is sparse.
DenseTable¶
The DenseTable object fulfills all private member methods stubbed out by the Table base class. The dense table contains a private member variable that is an instance of numpy.array. The array object is a matrix that contains all values including zeros. It is advised to use this table only if the number of samples and observations is reasonble. Unfortunately, it isn’t reasonable to define reasonable in this context. However, if either the number of observations or the number of samples is > 1000, it would probably be a good idea to rely on a SparseTable.
Table type objects¶
The table type objects define variables and methods specific to a table type. These inherit from a Container Class and a table type base class, and are therefore instantiable. Generally you’ll instantiate tables with biom.table.table_factory, and one of these will be passed as the constructor argument.
DensePathwayTable¶
- class biom.table.DensePathwayTable(*args, **kwargs)¶
Instantiatable dense pathway table
SparsePathwayTable¶
- class biom.table.SparsePathwayTable(*args, **kwargs)¶
Instantiatable sparse pathway table
DenseFunctionTable¶
- class biom.table.DenseFunctionTable(*args, **kwargs)¶
Instantiatable dense function table
SparseFunctionable¶
- class biom.table.SparseFunctionTable(*args, **kwargs)¶
Instantiatable sparse function table
DenseOrthologTable¶
- class biom.table.DenseOrthologTable(*args, **kwargs)¶
Instantiatable dense ortholog table
SparseOrthologTable¶
- class biom.table.SparseOrthologTable(*args, **kwargs)¶
Instantiatable sparse ortholog table
SparseGeneTable¶
- class biom.table.SparseGeneTable(*args, **kwargs)¶
Instantiatable sparse gene table


Figure
5.8.1
.
Percentage of total global CO2 emissions since 1750. EU-27 contains emissions from the 27 nations of the European Union in 2021, with the United Kingdom and Russia (and other nations) in Europe (excl. EU-27). Image CC-BY 4.0 by Hannah Ritchie
[<IP_ADDRESS>]
.
Image Description 16
.
in-context


| UPCOMING PERFORMANCES
|
A Cappella Night Live 2006
Join us and 10 other a cappella groups for a night of rockin' a cappella. All audience members will assist in selecting the a cappella participants for the August 2006 Cornell Night. |
|
Statler Auditorium |
Friday, Feb 25, 2006 @ 8:00pm |
ICCA Quarterfinals
Come cheer us on as we compete in the International Championship of Collegiate A Cappella here at Cornell! This competition will be the quarterfinals for the Midatlantic region and will include:
|
|
Sage Chapel |
Saturday, Feb 26, 2005 @ 8:00pm |
Cuttin' Class XXII
Come to our annual Spring show: Cuttin' Class XXII. The night will feature tight harmonies, hilarious skits, and really ridiculously good looking singers, so you won't want to miss out. |
| Statler Auditorium |
Saturday, Apr 22, 2006 @ 8:00pm |
SPRING AUDITIONS
Come be a part of Cornell's Original Coed A Cappella Group! |


| Mixin | Description |
|---|---|
surface |
Mandatory. Adds base styles for a ripple surface |
radius-bounded($radius) |
Adds styles for the radius of the ripple effect, for bounded ripple surfaces |
radius-unbounded($radius) |
Adds styles for the radius of the ripple effect, for unbounded ripple surfaces |
| Mixin | Description |
|---|---|
states($color, $has-nested-focusable-element) |
Mandatory. Adds state and ripple styles in the given color |
states-activated($color, $has-nested-focusable-element)
|
Optional. Adds state and ripple styles for activated states in the given color |
states-selected($color, $has-nested-focusable-element)
|
Optional. Adds state and ripple styles for selected states in the given color |
| Mixin | Description |
|---|---|
states-base-color($color) |
Mandatory. Sets up base state styles using the provided color |
states-opacities($opacity-map, $has-nested-focusable-element)
|
Sets the opacity of the ripple in any of the hover ,
focus , or press states. The
opacity-map can specify one or more of these states as
keys. States not specified in the map resort to default opacity
values. |
| Function | Description |
|---|---|
states-opacity($color, $state) |
Returns the appropriate default opacity to apply to the given color in the given state (hover, focus, press, selected, or activated) |


[23112654A]
SPLD_ME
Modern 2 -
no i tag
but has multiple
breaks
[06010709A]
SPLD_XO
Modern 2 - no i tag - no breaks


ClassificationFunctions
¶
Functions related to the process of classifying the SDSS spectra
-
ClassificationFunctions.
classify
(model, data)[source]¶ Runs the classifier and returns the predicted classes
Parameters: model (sklearn model object) – Returns: - classes (pd.DataFrame) – Dataframe containing all the predicted classes
- classA (pd.DataFrame) – Dataframe with the results that have class case A
- percent (float) – Percentage of the spectra which are found to match column densities < 17.2
-
ClassificationFunctions.
compile_data_set
(file_dir='./data/lines/', save_name='./data/sdss_df.pkl', clobber=False)[source]¶ Compiles the SDSS data set
Parameters: - file_dir (str) – Directory to the individual SDSS line data files
- save_name (str) – Name of the savefile
- clobber (bool) – Whether to overwrite an already compiled file


Since the temperature has the biggest visual effect on the electricity load, we're now trying to
use temperature as the x-axis to separate the factor and see more details.
Hover on one of the labels, we can see corresponding data in a highlighted form.
Focusing on temperature only, we can find out that both high temperature and low temperature
have a positive relation with the electricity load. What's more, a high-temperature situation
stands out more significantly.
Focusing on weekends and normal days (weekdays without other conditions),
it's clearly illustrated that weekends mostly distribute below the normal days' boundary despite
the
temperature factor.
The short rest for people and their companies contributes to the reduction on electricity usage.
While answering the 'What' question, part of the answer to 'How' is also revealed.
Both natural(temperature) and human(weekends) factors contribute to the abnormality in
electricity consumption. For the temperature factor, the electricity usage tends to go higher
when it gets closer to either end of its extremes(cold or hot).


Suppose that everyone in a country decided to reduce the amount of meat consumption on a given year. How much would that simple measure impact our greenhouse gases emissions?
Select a country* and check the compound effects of reducing the meat consumption over the years.
Consequences of reducing meat consumption by the selected amounts:
CO2 emissions would fall by tons
(% of the total world's CO2 emissions in a year)
This is equivalent to the emissions of cars in a year!
And the proposed reductions mean each person would only have to eat less
grams of beef,
grams of pork,
grams of poultry,
grams of lamb per day!
Data sources: OECD, Our World in Data, US Environmental Protection Agency
*Data does not include breakdown of food consumption for European countries from OECD


Yes, This Gundam Helmet Was Knitted
by Brian AshcraftTwitter user Namio’s hobby isn’t just knitting, but knitting woolen Gundam helmets.
It took days to knit each helmet. The RX-178 Gundam Mk-II helmet, for example, took four days, with Namio knitting morning to night for two of those. The result is glorious. Perfect for winter, too.
Yes, that’s a knitted face mask. With the helmet, apparently it’s very warm—hot, even.
And when the helmet is not being worn, it’s perfect for bewildering cats.


causal_data_augmentation.causal_data_augmentation¶
causal_data_augmentation.causal_data_augmentation.api¶
Versions of the method (corresponding to different modeling choices, subroutines, etc.) will be defined in this layer.
- class EagerCausalDataAugmentation(method_config=FullAugment(weight_threshold=None, weight_threshold_type='total', normalize_threshold_by_data_size=True, weight_kernel_cfg={}))¶
Bases:
object
Implementation of Causal Data Augmentation. Augments the data and returns the augmented data (i.e., not lazy = eager). Suitable for those predictor classes that go better with one-time data augmentation than on-the-fly augmentation.
Constructor.
- Parameters
method_config (
AugmenterConfig
) – the config of the method.
- augment(data, estimated_graph)¶
Generate augmented data. Does not consider overlapping, etc., against the original data.
- Parameters
data (
DataFrame
) – The source domain data to be used for fitting the novelty detector.estimated_graph (
ADMG
) – The ADMG object used for performing the augmentation.
- Returns
Tuple of
(augmented_data, weights)
: ifself.sampling_method
is'full'
.augmented_data : if
self.sampling_method
is'stochastic'
.
- Return type
One of the following
Examples
>> weight_threshold = 1e-5 >> augmenter = EagerCausalDataAugmentation(FullAugment(weight_threshold)) >> raise NotImplementedError()
- validate_config(method_config)¶
Check the validity of the method config.
- Parameters
method_config (
AugmenterConfig
) – Method configuration to be validated.- Return type
None
Module contents


Dear OpenEnergyMonitor Community!
We are looking for some bright people (individuals or a group) who can build, program and customize 60 pcs. of energy monitoring sets (for AC voltage/current with time-stamp) for a Swiss demonstration project in distributed grid storage. This is a paid job.
InGrid is a Swiss start-up in the business of distributed grid storage. (http://www.ingridag.com) We are about to begin a demonstration project in autumn 2014 with 25 pcs. of mobile battery storage trailers, equipped with a battery (23 kWh) and a bidirectional inverter (12 kW) and our InGrid Box, which controls the charging and discharging of the battery by the inverter and does "grid-friendly" integration of renewable energies. -> https://www.youtube.com/watch?v=Fp8rbgB3nkI
The demonstration project layout: See: http://ingridag.com/cms/index.php?d-ingrid-ag-demonstrationsprojekt
This 25 storage sets shall move during 1.5 years 3 to 4 times to different grid knots (or grid "situations") where we will measure and demonstrate the added value of InGrid distributed grid storage. Of course we have to demonstrate this by measuring power flows and voltage.
The open energy monitoring system seems interesting.
What we need:
- 230V/400V and 50 Hz equipment
-
For each of the 25 trailers located at very different places (most of them equipped with PV-production)
- 3-phase voltage and current measurement with a time-stamp in the household where the trailer is plugged-in (power flows out of the house and into the house -> power flow direction must be detectable)
- 3-phase current measurement with a time stamp of the PV-production
- For calibrating the voltage and current measurement by the inverter in the trailer: 3 rather precise measurement sets, which can be integrated in the trailer
- For 10 MV/LV-Transformator stations: voltage and current measurements with a time-stamp on the LV-side and of the branches to which the battery trailers are connected to
What we then need is a "thing" that reads the measurement data of energy monitoring system and stores it onto some SD disk or similar. We do not have to visualize or control anything for the moment. Just pure and reliable data acquisition. We will collect the SD cards all few months and load this data onto a professional database, where we will evaluate the data.
Open questions:
- What would be the precision of this measurement devices? (precision of current / voltage measurements and of time deviation)
- One log every second?
- Can the time-stamp somehow be synchronized among the devices, so that we have time-synchronized readings of the energy flows and the voltage?
- I don't think we need internet or GPRS access to the measurement devices. But perhaps you have other ideas?
Finances:
- We buy all material and equipment necessary
- We pay the development and integration work necessary
- We have a budget for this.
Open Innovation: This development can be used and further developed for:
- "GridSpy" = distributed LV-grid monitoring (voltage measurements with time stamp -> send values or alarms to the utility (overvoltage problems caused by PV-production).
- Vizualization Software of grid status collecting measurement values of the GridSpy monitors -> Utilities are blind in the LV-grid today. As PV-Production grows, LV-grid problems grow with it. Such a monitoring system would help a lot (and if it's low cost, then no question to buy it immediately.
- Integration of the "GridSpy" in the InGrid-Box
- .....(up to your fantasy)
How would we proceed?
- Prototype measurement-set for evaluation in May/June 2014
- If reliable, precise enough, and according to specification: Build 60 sets in July/August
Interested in contributing to the energy future? Then please send a mail to max.ursin(at)ingridag.com
Best regards
Max
Re: Who can build an energy monitoring system for a Swiss distributed grid storage demonstration project?
Hi! I wanted to ask if this Project is still on an "open" Status. If so, we (WWW.sensme.at) would be highly interested in discussing cooperation possibilities. We developed a wireless (data Transfer and - optional- power) sensoric measurement System that could be the Basis or even the solution for your Needs. We are a Company cooperation- an Engineering Office for environmental Engineering and and Engineering Office for Electrical Communication Engineering. There is many years of experience in developing electronic Systems and Lithium polymer battery Systems made up for companies. If you are interested in further talks, please contact me. Project language can be english and german of course. We do not have a Problem to travel to Switzerland for Project Meetings and discussions.
Best regards,
Guenter Hoffellner (g.hoffellner(at)sensme.at)


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Before Change
f"{linters_with_multiple_reports}. The option `--lint-reports-dir` only works if "
f"each linter has a single result. {suggestion}"
)
reports = itertools.chain.from_iterable(linter_to_reports.values())
merged_reports = await Get(Digest, MergeDigests(report.digest for report in reports))
workspace.write_digest(merged_reports, path_prefix=lint_subsystem.reports_dir)
logger.info(f"Wrote lint result files to {lint_subsystem.reports_dir}.")
After Change
for field_set in request.field_sets
)
// We consolidate all results for each linter into a single `LintResults`.
all_results = tuple(
LintResults(
itertools.chain.from_iterable(
per_target_results.results for per_target_results in all_linter_results
),
linter_name=linter_name,
)
for linter_name, all_linter_results in itertools.groupby(
all_per_target_results, key=lambda results: results.linter_name
)
)
else:
all_results = await MultiGet(
Get(LintResults, LintRequest, lint_request) for lint_request in valid_requests
)


bit-dp
]
Leetcode 1787. Make the XOR of All Segments Equal to Zero
Notice, that the problem is equivalent to the following problem: we need to make a_0 = a_k = a_2k
, a_1 = a_(k+1) =
and so on. We can reformulate it: we have k
groups of numbers and we need to change minimum possible number of numbers, such that in each group we have equal numbers and XOR
of all groups is equal to 0
.
Define as dp[i][mask]
the maximum number of values we can not change, such that we have mask
is XOR of first i
groups (it is easier to work with maximums, not minimums, because groups can have different size).
Then, let us calculate i
-th row of matrix dp
, if we know i-1
-th row. Then we can have two options:
- On the last step we take
mask
, which is not in numbers ofdp[i-1]
row, it this case we need to choose maximum fromdp[i-1]
- Or we can take
mask
, which is inside groupi-1
of our numbers, in this case we need to checkdp[i-1][j^mask] + freq[i-1][mask]
.
Complexity: denote by T
number of bits each number can be restricted and by n
length of nums
. We have 3
loops here, but loop for i
and for mask
will give you in total O(n)
complexity, and loop for j
will give you O(2^T)
complexity, so totally we have O(2^T n)
time complexity and O(2^T k)
space complexity.
class Solution:
def minChanges(self, nums, k):
N = 1<<10
freq = defaultdict(Counter)
for i, num in enumerate(nums):
freq[i%k][num] += 1
dp = [[0] * N for _ in range(k+1)]
for i in range(1, N): dp[0][i] = -float("inf")
for i in range(1, k+1):
max_row = max(dp[i-1])
for j in range(N):
for mask in freq[i-1]:
dp[i][j] = max(dp[i][j], max_row, dp[i-1][j^mask] + freq[i-1][mask])
return len(nums) - dp[-1][0]
Shorter version, using greedy idea
This solution is inspired by delphih solution https://leetcode.com/problems/make-the-xor-of-all-segments-equal-to-zero/discuss/1097266/Python-Solution-with-some-explanation, please go and upvote this post!
First of all, it can be proven, that one of the two cases holds:
- All numbers except one in each group are numbers from this groups.
- All numbers in each group are numbers from this group.
It can be proven, that it can not happen, that we have 2
or more numbers from outside groups, I will write it a later if I have time.
For the first one, we need to check groups with the maximum frequencies of elements and choose the k-1
most frequent.
For the second case, we can use almost the same logic we have previously, but now we do not need to think about numbers outside groups! So we just have case 1
from previous algorithm and we can forget about case 2
.
Complexity: what is important, complexity is the same as it was previously: O(2^T n)
time complexity and O(2^T k)
space complexity.
class Solution:
def minChanges(self, A, k):
freq = [Counter(A[j] for j in range(i, len(A), k)) for i in range(k)]
mxs = [freq[i].most_common(1)[0][1] for i in range(k)]
@lru_cache(None)
def dp(i, j):
if i == 0: return -10000*j
return max(dp(i-1,j^m) + freq[i-1][m] for m in freq[i-1])
return len(A) - max(sum(mxs) - min(mxs), dp(k, 0))


TICO-19 TRANSLATION INITIATIVE FOR COVID-19
The current pandemic has once again proven the importance of translation, especially in these times marked by uncertainty and misinformation. During any crisis, nothing is more important than fast and accurate communication. The reality is that official COVID-19 news is only available in a few major languages. But what about the millions of people who don't speak those and who also need information in their native language? How do we reach them? Providing timely translated information to everyone is a huge challenge requiring a joint and concerted effort. In order to make it possible to translate crisis-related content faster, Translators without Borders, academic and industry partners have started this initiative.
RESOURCES FOR TRANSLATORS
To date, the consortium has collected, curated and translated multiple resources that are now available to the wider professional translation community. Our aim is that these resources will help to ensure that, no matter the language, vital information can be generated, accessed and shared by all.
TRANSLATION MEMORIES
Translation memories for a vast amount of language pairs are ready for use and available in .TMX format.
Download your TM.
TRANSLATED TERMINOLOGY
Get quick access to COVID-19 terminology. Glossaries are currently offered in 127 languages!
Download a glossary
TRANSLATION BENCHMARK
The TICO-19 Translation Benchmark is a resource for those interested in the assessment of MT output quality.
Learn more
AND MORE...
For a range of additional resources from our contributors.
Visit the resources page.
BENEFITS OF USING TICO-19 RESOURCES
All TICO-19 resources have been prepared and reviewed by paid professional translators in order to guarantee that they are of the highest accuracy.
Consistency is especially important within the medical field due to the problematic nature of misinterpreted words or meanings.
When time is of the essence, having well-prepared resources at hand can make a huge difference in expediting translations.
SUPPORT TICO-19
Volunteer
Volunteer with Translators without Borders (TWB) if you are fluent in a second language.
Whether you are interested in translating medical or crisis response texts, there are engaging projects available to suit all preferences.
Professional translators are especially encouraged to apply.
Translate
Contribute by translating the TICO-19 Benchmark and ultimately help us to cover as many of the world’s languages as possible!
As a large portion of the benchmark includes medical terminology, we strongly encourage that professional translators handle the technical content, and that you follow a rigorous process of Quality Assurance.
Share
If you are a professional translator and have already produced COVID-19 related content, we invite you to share your translation memories, terminology and glossaries. Feel free to also share your feedback on any errors spotted in our published terminologies and suggestions on what translators on the ground need.
MORE INFORMATION
TICO-19 is moving to its next phase of creating more resources in more languages. We would like to invite you to collaborate and achieve this goal together!
To get more details on our QA process, to learn how to share your translation memory and glossary, or to provide feedback and suggestions, don’t hesitate to email us.
For more detailed information, please read the published paper (PDF):
TICO-19: the Translation Initiative for COvid-19
TICO-19 PARTNERS


- solar_azimuth() (astral.Astral method)
- (astral.City method)
- solar_depression (astral.Astral attribute)
- solar_elevation() (astral.Astral method)
- (astral.City method)
- solar_noon() (astral.City method)
- solar_noon_utc() (astral.Astral method)
- sun() (astral.City method)
|
- sun_utc() (astral.Astral method)
- sunrise() (astral.City method)
- sunrise_utc() (astral.Astral method)
- sunset() (astral.City method)
- sunset_utc() (astral.Astral method)
|


|
|||||||||
| PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES |
| Class Summary | |
|---|---|
| AntXmlParsedData | Special version of XmlParsedData for Ant build files. |
| CharSequenceReader | implement a Reader on top of CharSequence, not only String like java.io.StringReader. |
| Debug | some debug flags for the XML plugin |
| DTDGenerator | DTDGenerator Generates a possible DTD from an XML document instance. |
| EditTagDialog | |
| ElementUtil | |
| MvnXmlParsedData | Special version of XmlParsedData for Maven pom.xml files. |
| PathUtilities | pathToURL() and urlToPath() |
| Resolver | Resolver grabs and caches DTDs and xml schemas. |
| Resolver.Entry | |
| SchemaMappingManager | |
| SchemaMappingManager.LoggingErrorHandler | logs errors and warnings to Activity Log and rethrow exception on error and fatalError to prevent invalid documents to result in invalid SchemaMappings. |
| SideKickTagHighlight | StructureMatcher finding the matching tag (open/close). |
| TagHighlight | StructureMatcher finding the matching tag (open/close). |
| TemplatesManager | install templates bundled with XMLPlugin into the relevant directory and tell Templates plugin to refresh the list. |
| TldXmlParsedData | Special version of XmlParsedData for tld files. |
| XmlActions | |
| XmlInsert | |
| XmlListCellRenderer | displays an icon corresponding to the kind of item and a preview for entities. |
| XmlListCellRenderer.CDATA | |
| XmlListCellRenderer.ClosingTag | |
| XmlListCellRenderer.Comment | |
| XmlListCellRenderer.WithLabel<Q> | store a completion with its label for preview |
| XmlParsedData | Encapsulates the results of parsing a buffer, either using Xerces or the Swing HTML parser. |
| XmlPlugin |
|
|||||||||
| PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES |


About Me
My name is Garrett Lee. I am from Tallahassee, FL, but I now live in Peachtree Corners, GA. I originally went to Florida State University and graduated with a degree in English which, at the time, was just a stepping stone towards film school. I went to Full Sail University in Orlando, FL and graduated after a couple of years. I found my first job in the film industry pretty quickly, but just as quickly realized that that lifestyle was not for me. I have been through a lot at this point and have now landed in the Georgia Tech Coding Bootcamp, seeking a new career path for myself in Web Development.
This page is one of my first projects that I have created from scratch. It is also one of the projects that I will continue to update in the future as I add new pieces to my portfolio. Keep checking back for more updates!
Contact Me by:
Phone Number:<PHONE_NUMBER>
Email: rgl10d@gmail.com


Home
Constraint Templates
Constraints
Configurations
Welcome!
Gatekeeper Policy Manager is a simple to use web-based tool to see the policies deployed in your cluster and their status
See Constraints status
{% if config.AUTH_ENABLED == 'OIDC' %}
Authentication is enabled
You'll be asked to login when proceeding
{% endif %}
{% include 'footer.html' %}


Sports broadcaster Kevin Harlan gives dramatic play-by-play of black cat's MetLife Stadium invasion
by Ryan GaydosSports radio broadcasting great Kevin Harlan had some fun Monday night when a black cat invaded MetLife Stadium during the New York Giants’ game against the Dallas Cowboys.
The cat was spotted at midfield toward the end of the second quarter. Referees stopped play for about 2 minutes before the cat cleared the field.
BLACK CAT HALTS GIANTS-COWBOYS MONDAY NIGHT FOOTBALL GAME, LEAVING VIEWERS TRANSFIXED
While fans at the stadium and on social media were mystified by the feline, Harlan made sure the radio audience got the full dramatic experience.
Harlan called the cat’s invasion as if the animal was running a football into the end zone. He even threw in a sponsor during his broadcast call. He also likened the cat’s elusiveness to star running backs Saquon Barkley, of the Giants, and Ezekiel Elliott, of the Cowboys.
At this point of the game, the Giants were leading 9-3. The cat left the field and New York’s hopes and dreams went with it, it seemed.
COLIN KAEPERNICK SPENDS BIRTHDAY HELPING HOMELESS IN CALIFORNIA
New York would allow 10 points in the quarter to end the half down 13-12. Dallas then outscored the Giants 24-6 in the second half and won the game 37-18.
Giants safety Jabrill Peppers knew something would go awry once he saw that cat.
CLICK HERE FOR MORE SPORTS COVERAGE ON FOXNEWS.COM
“I ain’t superstitious or nothing but any time I saw a black cat or it went across me growing up, something bad happened,” Peppers said, according to the New York Post. “I kinda grew out of it now, but I still don’t like ’em.


{{ define "header" }} {{ end }} {{ define "main" }}
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When ordering or registering on our site, you may be asked to enter your email address or other details to help you with your experience.
We collect information from you when you register on our site, respond to a survey, fill out a form, use Live Chat, open a Support Ticket, or enter other information on our site.
The information we collect from you when you register, make a purchase, sign up for our newsletter, respond to a survey or marketing communication, surf the website, or engage certain other site features, may be used in the following ways:
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We have not enabled Google AdSense on our site, though we may do so in the future. We use them to compile data regarding user interactions to sell that data to interested parties.
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CalOPPA is the first state law in the nation to require commercial websites and online services to post a privacy policy. The law applies beyond California, requiring any person or company in the United States (and conceivably in the world) that operates websites collecting Personally Identifiable Information from California consumers to post a conspicuous privacy policy on its website stating exactly what information is being collected and with which individuals or companies it is being shared. For more information about CalOPPA, use the following link:
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In accordance with CalOPPA, we have provided the preceding information on this page about our privacy policies. Furthermore, we agree to the following:
When it comes to the collection of personal information from children under the age of thirteen years old, the Children's Online Privacy Protection Act (COPPA) puts parents in control. The Federal Trade Commission, the consumer protection agency of the United States, enforces the COPPA Rule, which stipulates what operators of websites and online services must do to protect children's privacy and safety online.
We will never knowingly request personally identifiable information from anyone under the age of thirteen without prior verifiable parental consent. If we become aware that a site visitor under the age of thirteen has provided personal data without prior verifiable parental consent, we will remove his or her personally identifiable information from our files. In order to remove your child's information please contact the following personnel:
We adhere to the following COPPA tenant:
The principles of the Fair Information Practices form the backbone of privacy law in the United States and the concepts they include have played a significant role in the development of data protection laws around the globe. Understanding and implementing the principles of the Fair Information Practice is critical to comply with the various privacy laws that protect personal information.
In accordance with Fair Information Practices, we will take responsive action should a data breach occur. We will notify you via email within seven business days of a breach. We will notify the users via in-site notification within seven business days of a breach.
Also in line with Fair Information Practices, we adhere to the Individual Redress Principle, which requires that individuals have the right to legally pursue enforceable rights against data collectors and processors who fail to adhere to the law. This principle states not only that individuals have enforceable rights against data users, but also that individuals have recourse to courts and/or government agencies to investigate and/or prosecute non-compliance by data processors.
The CAN-SPAM Act is a law that sets rules for commercial email, establishes requirements for commercial messages, gives recipients the right to stop emails from being sent to them, and stipulates tough penalties for violations.
We collect your email address in order to:
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If at any time you would like to unsubscribe from receiving future emails, you can follow the instructions at the bottom of each email to unsubscribe.
If there are any questions regarding this privacy policy, or regarding changes to account privacy settings, you may contact us using the information below.
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Last Edited on 07-27-2018


There is no denying that cryptocurrencies are getting more and more prominence in the Mass market. Low fees, decentralization and no need for intermediaries make crypto appealing for integration into daily life. You can easily transfer any amount of crypto all over the world in a matter of seconds for Ripple, Stellar, EOS, Tron, and just a few minutes for Bitcoin, and Ethereum.
When it comes to centralized institutions and banks, cross-border funds transfer becomes a complicated, time and fee consuming process. If you live, say, in China and need to move funds to your grandpa living in France, most likely it will take days and you will have to pay at least 5% from the total amount. If you need to convert USD to EUR, you will overpay 7%.
This is especially inconvenient for cross-border trading. If you're a merchant, would you like to loss hundreds of dollars monthly just wasting them on fees? The need to pay fees inevitably influences the prices of the goods you sell. Cryptocurrencies help solve this issue with the help of fast transactions and low fees. Still, not everywhere crypto is actively used due to volatility, scalability issues and other hurdles.
Cryptocurrency volatility is one of major obstacles in the way to mass adoption. You can buy Bitcoin for one rate and sell it the next day at another. The next big issue is transaction speed. If you're, say, a giant e-commerce company, you may have hundreds payments worldwide on a daily basis. If the blockchain gets overloaded, payments will be stuck for days to went through. The last, but not least issue is that you can't just get to the bank and exchange your crypto to the fiat currency.
Stable coins are designed to resolve these issues.
Stable Coins are cryptocurrencies with total amount pegged to other assets. (In the most cases it's USD or other cryptocurrency). Some examples: Tether, Gemini Dollar, USDC, PAX and DAI.
Stable coins work as a bridge between the world of crypto and the world of fiat currencies. The companies use some centralized authorities that back every coin with one-to-one equivalent in USD or Crypto and controls the price volatility.
For example Tether, one of the top cryptocurrencies nowadays. The project is made on the Omni Protocol Layer above the Bitcoin, Litecoin and Ethereum Blockchains.
Tether founders claim that every Tether coin (USDT) is backed with 1 US Dollar equivalent, but for all the years still didn't provide any official documentation or audits.
TrueUSD (TUSD). Another stablecoin that published a proof that every TUSD token is backed with $1.
TUSD is based only on the Ethereum blockchain, provides a legal protection to the TUSD holders and is opened to third-parties audits.
USDC is an Ethereum based stablecoin created by Coinbase and Circle company.
Paxos Standard Token (PAX). It's another stablecoin based on Ethereum blockchain. The project is recently received negative opinion for the backdoor in their codebase that allows PAX authorities to freeze accounts with funds if necessary.
DAI is an Ethereum based Stable Coin. It differes from other stablecoins by implementing a fully decentralized technology. It's price is stable price is backed by Ether, not USD.
StableCoins were created to solve the top problems of cryptocurrencies, such as price volatility and interaction with the real world money and markets.
To do so, Stable Coins companies make a partnership with a the issuer to ensure that the price will stay the same in any cases. That also allows to easily redeem crypto to USD with no limitations. Another benefit of stablecoins — they are customer friendly. You don't have to think about the rates of the crypto, there are low possibility to lose the funds because of the price changing, it's convenient system for cross border payments.
StableCoins are often used on major exchanges to transfer Bitcoin into USD.
If you want to store your StableCoins safely, install Atomic Wallet, a decentralized application with a built-in exchange and multi-asset support. Only you control your funds!
Note: We support all aforestated tokens except Tether Omni.


The new app tab feature in Firefox is great. I use it a lot... which has starkly illustrated how apps and tabs have very different use-cases and usage patterns. Often I will check my Gmail app tab because I see the glowing notification that a new email has arrived, do something (or nothing), and then pop back to where I was browsing... in one of those 78 tabs I have open?! The windowing model in operating systems allows me switch back and forth between contexts with ease. But app tabs do not:
- If I'm using my Gmail app tab and I didn't open any links while there, I can still see the last tab I was at, and click on it. But I have to use the mouse if I want to get directly back there.
- Out of sheer muscle memory and mouse-averseness, sometimes I can traverse tabs via the next/previous-tab keyboard shortcuts to get back to where I was. Sometimes it's a lot of tabs, so either I'll hold the arrow key down, speeding past the tab I wanted (and back and forth a few times), or I'll just hit the arrow key a bunch of times in quick succession. Both options are frustrating, slow and RSI-inducing.
Or I could expend mental energy to search in the awesomebar and switch to that tab, which often looks like this: "hm, type 'bug' and then try to remember some words in the bug summary, but those words match a bunch of other bugs, and i don't know the bug number, and also I'm on an attachment page because I'm reviewing a patch on the bug, so the summary won't be in the page title..." and on and on. Now add the fact that switch-to-tab rarely even shows up in the awesomebar for me, and well, a generally high level of fail with this option. Then there's link opening:
Links opened in app tabs are put at the beginning of your tabs, and the tab strip is animatedly scrolled there. Boom, instantly lost where I was before checking my email.
- We tried an experiment where they open at the end of the set of open tabs. I found that to have serious "out of sight, out of mind" problems. That experiment was rolled back. And it doesn't necessarily solve the context problem anyway.
- Both approaches cause excess amounts of whizzing animations, either when you want to "go around the horn" to get to the tabs you just opened from app tabs, or when you want to go to them and then get back to where you were.
- There's no right answer! Sometimes I see a link to a recipe in my Seesmic app tab that I'd like to open in that series of tabs related to food that I have open somewhere in the middle of my open tabs. The user is not in control of where these links are opened. I can't choose whether to open them at the beginning or end of the tab strip, or in a new tab group, or new window, etc. Part of me thinks that I actually might work best in a world where each app tab is bound to a single tab group, so that tab growth is bound to the source... but that's a vision for another day (and blog post and add-on). So I've made Peek, an add-on that's a hybrid solution: Instead of making you go to your app tabs, your app tabs will come to you. Peek allows you to open your app tabs in a floating panel that opens on top of wherever you are in your tabs. Links open to the right of whatever your current active tab is, and in the background, so that when you're finished peeking, you are exactly where you left off.
To use Peek, first create some app tabs. Then you can peek at them using the keyboard shortcut ALT+SHIFT+1-9 where the number corresponds with the order your app tabs are in. To stop peeking, hit escape (or switch apps or anything else that takes focus away from the panel).
INSTALL. Peek is a Jetpack add-on that does not require a restart of the browser. User beware: This is an experimental add-on - I've been using it on Nightly builds, but haven't done much testing elsewhere.
Benefits: