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ACM Digital Library:
ACM Transactions on Graphics (TOG) Volume 35, Issue 4 (July 2016) Proceedings of ACM SIGGRAPH 2016
ACM Digital Library (DOI) Link for the paper
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Demo Program or Source Code
Related Links
Changelog
- The Diffractive Achromat: Full-Spectrum Computational Imaging With Diffractive Optics
-
Yifan (Evan) Peng
(The University of British Columbia),
Qiang Fu
(King Abdullah University Of Science And Technology),
Felix Heide
(The University of British Columbia),
Wolfgang Heidrich
(King Abdullah University Of Science And Technology)
- Practical Multispectral Lighting Reproduction
-
Chloe LeGendre,
Xueming Yu
Dai Liu,
Jay Busch,
Andrew Jones
(USC Institute for Creative Technologies),
Sumanta Pattanaik
(University of Central Florida),
Paul Debevec
(USC Institute for Creative Technologies)
- Computational Imaging With Multi-Camera Time-of-Flight Systems
-
Shikhar Shrestha,
Felix Heide
(Stanford University,
Wolfgang Heidrich
(King Abdullah University Of Science And Technology),
Gordon Wetzstein
(Stanford University)
- Occluded Imaging With Time-of-Flight Sensors
(TOG Paper)
-
Achuta Kadambi,
Hang Zhao
(MIT Media Lab),
Boxin Shi
(MIT Media Lab and Nanyang Technological University),
Ramesh Raskar
(MIT Media Lab)
- Example-based Plastic Deformation of Rigid Bodies
-
Ben Jones
(University of Denver),
Nils Thuerey
(TU Munich),
Tamar Shinar
(University of California - Riverside),
Adam Bargteil
(University of Maryland)
- Pose-Space Subspace Dynamics
-
Hongyi Xu,
Jernej Barbic
(University of Southern California)
- Efficient Dynamic Skinning with Low-Rank Helper Bone Controllers
-
Tomohiko Mukai
(Tokai University),
Shigeru Kuriyama
(Toyohashi University of Technology)
- Real-time Skeletal Skinning with Optimized Centers of Rotation
-
Binh Huy Le
(Disney Research Pittsburgh),
Jessica Hodgins
(Carnegie Mellon University and Disney Research Pittsburgh)
- Erosion Thickness on Medial Axes of 3D Shapes
-
Yajie Yan
(Washington University in St Louis),
Kyle Sykes
Erin Chambers,
David Letscher
(St. Louis University),
Tao Ju
(Washington University in St Louis)
- Q-MAT: Computing Medial Axis Transform Using Quadratic Error Minimization
(TOG Paper)
-
Pan Li,
Bin Wang
(Tsinghua University, Beijing),
Feng Sun
(The Pennsylvania State University),
Xiaohu Guo
(University of Texas at Dallas),
Wenping Wang
(The University of Hong Kong)
- Mesh Arrangements for Solid Geometry
-
Qingnan Zhou
(New York University),
Eitan Grinspun
(Columbia University),
Denis Zorin
(New York University),
Alec Jacobson
(Columbia University)
- Animated Mesh Approximation With Sphere-Meshes
(TOG Paper)
-
Jean-Marc Thiery
(TU Delft),
Emilie Guy,
Tamy Boubekeur
(Telecom ParisTech)
Elmar Eisemann
(TU Delft)
- Integration with Stochastic Point Processes
-
Cengiz Oztireli
(ETH Zurich)
- Fast 4D Sheared Filtering for Interactive Rendering of Distribution Effects
(TOG Paper)
-
Ling-Qi Yan,
Soham Uday Mehta
(University of California at Berkeley),
Ravi Ramamoorthi
(University of California, San Diego),
Fredo Durand
(MIT CSAIL)
- Adaptive Polynomial Rendering
-
Bochang Moon,
Steven McDonagh,
Kenny Mitchell,
Markus Gross
(Disney Research Zurich)
- Real-Time Polygonal-Light Shading with Linearly Transformed Cosines
-
Eric Heitz,
Jonathan Dupuy
(Unity Technologies),
Stephen Hill
(Ubisoft),
David Neubelt
(Ready At Dawn Studios)
- Adjoint-Driven Russian Roulette and Splitting in Light-Transport Simulation
-
Jiri Vorba
(Weta Digital Ltd and Charles University in Prague),
Jaroslav Krivanek
(Charles University in Prague)
- Computational Thermoforming
-
Christian Schuller
(ETH Zurich),
Daniele Panozzo
(ETH Zurich and New York University),
Anselm Grundhofer
(Disney Research),
Henning Zimmer,
Evgeni Sorkine,
Olga Sorkine-Hornung
(ETH Zurich)
- Procedural Voronoi Foams for Additive Manufacturing
-
Jonas Martinez,
Jeremie Dumas,
Sylvain Lefebvre
(INRIA)
- Printed Perforated Lampshades for Continuous Projective Images
(TOG Paper)
-
Haisen Zhao,
Lin Lu
Yuan Wei
(Shandong University),
Dani Lischinski
(The Hebrew University of Jerusalem),
Andrei Sharf
(Ben-Gurion University),
Daniel Cohen-Or
(Tel Aviv University),
Baoquan Chen
(Shandong University)
- Pushing the Limits of 3D Color Printing: Error Diffusion With Translucent Materials
(TOG Paper)
-
Alan Brunton,
Can Ates Arikan,
Philipp Urban
(Fraunhofer Institute for Computer Graphics Research IGD)
- CofiFab: Coarse-to-Fine Fabrication of Large 3D Objects
-
Peng Song
(University of Science and Technology of China),
Bailin Deng
(University of Hull),
Ziqi Wang,
Zhichao Dong,
Wei Li
(University of Science and Technology of China),
Chi-Wing Fu
(The Chinese University of Hong Kong),
Ligang Liu
(University of Science and Technology of China)
- RAID: A Relation-Augmented Image Descriptor
-
Paul Guerrero,
Niloy Mitra
(University College London),
Peter Wonka
(Arizona State University)
- Learning How Objects Function via Co-Analysis of Interactions
-
Ruizhen Hu
(Shenzhen University),
Oliver van Kaick
(Carleton University),
Bojian Wu
(SIAT),
Hui Huang
(Shenzhen University and SIAT)
Ariel Shamir
(The Interdisciplinary Center),
Hao Zhang
(Simon Fraser University)
- PATEX: Exploring Pattern Variations
-
Paul Guerrero,
(University College London),
Gilbert Bernstein
(Stanford University)
Wilmot Li
(Adobe Research),
Niloy Mitra
(University College London)
- 3D Mesh Labeling via Deep Convolutional Neural Networks
(TOG Paper)
-
Kan Guo,
Dongqing Zou,
Xiaowu Chen
(Beihang University)
- Efficient 3D Object Segmentation from Densely Sampled Light Fields with Applications to 3D Reconstruction
(TOG Paper)
-
Kaan Yucer
(ETH Zurich and Disney Research Zurich),
Alexander Sorkine-Hornung,
Oliver Wang
(Disney Research Zurich),
Olga Sorkine-Hornung
(ETH Zurich)
- A Compiler for 3D Machine Knitting
-
James McCann
(Carnegie Mellon University and The Walt Disney Company),
Lea Albaugh,
Vidya Narayanan
(The Walt Disney Company and Disney Research),
April Grow
(University of California, Santa Cruz / The Walt Disney Company / Disney Research),
Wojciech Matusik
(Massachusetts Institute of Technology),
Jennifer Mankoff
(Disney Research Pittsburgh and Carnegie Mellon University),
Jessica Hodgins
(Carnegie Mellon University / The Walt Disney Company / Disney Research Pittsburgh)
- Physics-driven Pattern Adjustment for Direct 3D Garment Editing
-
Aric Bartle
(Stanford University),
Alla Sheffer
(University of British Columbia),
Vladimir G. Kim,
Danny Kaufman
(Adobe Research),
Nicholas Vining
(University of British Columbia),
Floraine Berthouzoz
(Adobe Research)
- Matching Real Fabrics With Micro-Appearance Models
(TOG Paper)
-
Pramook Khungurn,
Daniel Schroeder,
Shuang Zhao,
Steve Marschner,
Kavita Bala
(Cornell University)
- Fitting Procedural Yarn Models for Realistic Cloth Rendering
-
Shuang Zhao
(University of California, Irvine),
Fujun Luan,
Kavita Bala
(Cornell University)
- Tactile Mesh Saliency
-
Manfred Lau,
Kapil Dev
(Lancaster University),
Weiqi Shi,
Julie Dorsey,
Holly Rushmeier
(Yale University)
- Perceptual Effect of Shoulder Motions on Crowd Animations
-
Ludovic Hoyet
(INRIA),
Anne-Helene Olivier,
Richard Kulpa
(Universite Rennes 2),
Julien Pettre
(INRIA)
- Body Talk: Crowdshaping Realistic 3D Avatars with Words
-
Stephan Streuber
(EPFL),
Quiros-Ramirez
(MPI for Intelligent Systems),
Matthew Hill,
Carina Hahn
(University of Texas at Dallas),
Silvia Zuffi
(IMATI-CNR),
Alice O'Toole
(University of Texas at Dallas),
Michael Black
(MPI for Intelligent Systems)
- An Interaction-Aware, Perceptual Model for Non-Linear Elastic Objects
-
Michal Piovarči
(Saarland University, MMCI and Comenius University),
David I.W. Levin
(Disney Research Pittsburgh),
Jason Rebello
(Harvard University),
Desai Chen
(MIT CSAIL),
Roman Ďurikovič
(Comenius University),
Hanspeter Pfister
(Harvard University),
Wojciech Matusik
(MIT CSAIL),
Piotr Didyk
(Saarland University, MMCI and MPI Informatik)
- Position-Normal Distributions for Efficient Rendering of Specular Microstructure
-
Ling-Qi Yan
(University of California at Berkeley),
Milos Hasan
(Autodesk),
Steve Marschner
(Cornell University),
Ravi Ramamoorthi
(University of California, San Diego)
- Multi-Scale Rendering of Scratched Materials Using a Structured SV-BRDF Model
-
Boris Raymond
(Universite de Bordeaux),
Gael Guennebaud,
Pascal Barla
(Inria)
- Multiple-Scattering Microfacet BSDFs with the Smith Model
-
Eric Heitz,
Johannes Hanika
(Karlsruhe Institute of Technology),
Eugene d'Eon
(8i),
Carsten Dachsbacher
(Karlsruhe Institute of Technology)
- Predicting Appearance From Measured Microgeometry of Metal Surfaces
(TOG Paper)
-
Zhao Dong*,
Bruce Walter*,
Steve Marschner,
Donald P. Greenberg
(Cornell University)
(*Joint first authors.)
- Cinema 3D: Large Scale Automultiscopic Display
-
Netalee Efrat
(The Weizmann Institute of Science),
Piotr Didyk
(MPI Informatik and Saarland University, MMCI),
Mike Foshey,
Wojciech Matusik
(MIT CSAIL),
Anat Levin
(The Weizmann Institute of Science)
- High Brightness HDR Projection Using Dynamic Freeform Lensing
(TOG Paper)
-
Gerwin Damberg,
James Gregson
(The University of British Columbia),
Wolfgang Heidrich
(King Abdullah University Of Science And Technology and The University of British Columbia)
- Additive Light-Field Displays: Realization of Augmented Reality With Holographic Optical Elements
-
Seungjae Lee,
Changwon Jang,
Seokil Moon,
Jaebum Cho,
Byoungho Lee
(Seoul National University)
- Fairy Lights in Femtoseconds: Aerial and Volumetric Graphics Rendered by a Focused Femtosecond Laser Combined With Computational Holographic Fields
(TOG Paper)
-
Yoichi Ochiai
(University of Tsukuba),
Kota Kumagai
(Utsunomiya University),
Takayuki Hoshi
(Nagoya Institute of Technology),
Jun Rekimoto
(The University of Tokyo and Sony CSL),
Satoshi Hasegawa,
Yoshio Hayasaki
(Utsunomiya University)
- Generating Dynamically Feasible Trajectories for Quadrotor Cameras
-
Mike Roberts,
Pat Hanrahan
(Stanford University)
- Roto++: Accelerating Professional Rotoscoping using Shape Manifolds
-
Wenbin Li
(University College London),
Fabio Viola
(Google DeepMind and The Foundry),
Jonathan Starck
(The Foundry),
Gabriel J. Brostow
(University College London),
Neill D.F. Campbell
(University of Bath and University College London)
- Rich360: Optimized Spherical Representation from Structured Panoramic Camera Arrays
-
Jungjin Lee, Bumki Kim, Kyehyun Kim, Younghui Kim,
Junyong Noh
(KAIST)
- Mapping Virtual and Physical Reality
-
Qi Sun
(Stony Brook University),
Li-Yi Wei
(University of Hong Kong)
Arie E. Kaufman
(Stony Brook University)
- A Non-Parametric Factor Microfacet Model for Isotropic BRDFs
-
Mohammed Bagher Mahdi
(Universite de Montreal, Canada),
John Snyder
(Microsoft Research),
Derek Nowrouzezahrai
(Universite de Montreal, Canada)
- Reflectance Modeling by Neural Texture Synthesis
-
Miika Aittala
(Aalto University),
Timo Aila
(NVIDIA Corporation),
Jaakko Lehtinen
(NVIDIA Corporation and Aalto University)
- ZoeMatrope: A System for Physical Material Design
-
Leo Miyashita,
K. Ishihara,
Yoshihiro Watanabe,
Masatoshi Ishikawa
(University of Tokyo)
- Emulating Displays with Continuously Varying Frame Rates
-
Krzysztof Templin
(MPI Informatik),
Piotr Didyk
(MPI Informatik and Saarland University, MMCI),
Karol Myszkowski,
Hans-Peter Seidel
(MPI Informatik)
- GazeStereo3D: Seamless Disparity Manipulations
-
Petr Kellnhofer
(MIT CSAIL and MPI Informatik),
Piotr Didyk
(MPI Informatik and Saarland University, MMCI),
Karol Myszkowski
(MPI Informatik),
Mohamed M. Hefeeda
(Qatar Computing Research Institute),
Hans-Peter Seidel
(MPI Informatik),
Wojciech Matusik
(MIT CSAIL)
- Binary-Continuous Image Decomposition for Multi-view Display
-
Gou Koutaki
(Kumamoto University)
- Seamless Visual Sharing with Color-Vision Deficiencies
-
Wuyao Shen,
Xiangyu Mao,
Xinghong Hu,
Tien-Tsin Wong
(The Chinese University of Hong Kong)
- Wasserstein Barycentric Coordinates: Histogram Regression Using Optimal Transport
-
Nicolas Bonneel
(Univ. Lyon, CNRS, LIRIS),
Gabriel Peyre
(CNRS, Univ. Paris-Dauphine),
Marco Cuturi
(Kyoto University)
- Entropic Metric Alignment for Correspondence Problems
-
Justin Solomon
(Princeton University),
Gabriel Peyre
(CNRS),
Vladimir Kim
(Adobe Research),
Suvrit Sra
(Massachusetts Institute of Technology)
- Point Registration via Efficient Convex Relaxation
-
Haggai Maron,
Nadav Dym,
Itay Kezurer,
Shahar Kovalsky,
Yaron Lipman
(Weizmann Institute of Science)
- Bijective Maps from Simplicial Foliations
-
Marcel Campen,
Claudio Silva,
Denis Zorin
(New York University)
- Globally Optimal Toon Tracking
-
Haichao Zhu,
Xueting Liu,
Tien-Tsin Wong,
Pheng-Ann Heng
(The Chinese University of Hong Kong)
- Resolving Fluid Boundary Layers With Particle-Strength Exchange and Weak Adaptivity
-
Xinxin Zhang,
Minchen Li
(The University of British Columbia),
Robert Bridson
(Autodesk, Inc. and The University of British Columbia)
- Schrodinger's Smoke
-
Albert Chern
(California Institute of Technology),
Felix Knoppel,
Ulrich Pinkall
(Technische Universitat Berlin),
Peter Schroder
(California Institute of Technology),
Steffen Weismann
(Google Inc.)
- Surface-Only Liquids
-
Fang Da
(Columbia University),
David Hahn
(Institute of Science and Technology Austria (IST Austria)),
Christopher Batty
(University of Waterloo),
Chris Wojtan
(Institute of Science and Technology Austria (IST Austria)),
Eitan Grinspun
(Columbia University)
- Efficient Fluid Simulation on the Surface of a Sphere
(TOG Paper)
-
David Hill,
Ronald D. Henderson
(DreamWorks Animation)
- Multiphase SPH Simulation for Interactive Fluids and Solids
-
Xiao Yan,
Yun-Tao Jiang,
Chen-Feng Li
(Swansea University),
Ralph R. Martin
(Cardiff University),
Shi-Min Hu
(Tsinghua University, Beijing)
- Unified Motion Planner for Fishes with Various Swimming Styles
-
Daiki Satoi
Mikihiro Hagiwara,
Akira Uemoto,
Hisanao Nakadai,
Junichi Hoshino
(University of Tsukuba)
- Guided Learning of Control Graphs for Physics-Based Characters
(TOG Paper)
-
Libin Liu
(Disney Research, Pittsburgh),
Michiel van de Panne
(University of British Columbia),
KangKang Yin
(National University of Singapore)
- Terrain-Adaptive Locomotion Skills Using Deep Reinforcement Learning
-
Xue Bin Peng,
Glen Berseth,
Michiel van de Panne
(University of British Columbia)
- Task-based Locomotion
-
Shailen Agrawal,
Michiel van de Panne
(University of British Columbia)
- Image Perforation: Automatically Accelerating Image Pipelines by Intelligently Skipping Samples
(TOG Paper)
-
Liming Lou
(Shandong University and University of Virginia),
Paul Nguyen
(University of Virginia),
Jason Lawrence
(University of Virginia and Google),
Connelly Barnes
(University of Virginia)
- Automatically Scheduling Halide Image Processing Pipelines
-
Ravi Teja Mullapudi,
Jonathan Ragan-Kelley
(Stanford University),
Andrew Adams
(MIT CSAIL),
Dillon Sharlet
(),
Kayvon Fatahalian
(Carnegie Mellon University)
- ProxImaL: Efficient Image Optimization using Proximal Algorithms
-
Felix Heide
(Stanford University and The University of British Columbia),
Steven Diamond,
Jonathan Ragan-Kelley,
Matthias Niesner
(Stanford University),
Wolfgang Heidrich
(The University of British Columbia),
Gordon Wetzstein
(Stanford University)
- Rigel: Flexible Multi-Rate Image Processing Hardware
-
James Hegarty,
Ross Daly,
Zachary DeVito,
Jonathan Ragan-Kelley,
Pat Hanrahan
(Stanford University)
- Reconciling Elastic and Equilibrium Methods for Static Analysis
(TOG Paper)
-
Hijung Shin,
Christopher Porst
(Massachusetts Institute of Technology),
Etinne Vouga
(The University of Texas at Austin),
John Ochsendorf
Fredo Durand
(Massachusetts Institute of Technology)
- Computational Design of Stable Planar-Rod Structures
-
Eder Miguel,
Mathias Leputre,
Bernd Bickel
(IST Austria)
- Non-Linear Shape Optimization Using Local Subspace Projections
-
Przemyslaw Musialski*,
Christian Hafner*,
Florian Rist,
Michael Birsak,
Michael Wimmer**
(Vienna University of Technology),
Leif Kobbelt**
(RWTH Aachen University)
(*: Joint first authors, **:Joint last authors)
- Acoustic Voxels: Computational Optimization of Modular Acoustic Filters
-
Dingzeyu Li
(Columbia University),
David Levin
(Massachusetts Institute of Technology and The Walt Disney Company),
Wojciech Matusik
(Massachusetts Institute of Technology),
Changxi Zheng
(Columbia University)
- Beyond Developable: Computational Design and Fabrication with Auxetic Materials
-
Mina Konakovic
(EPFL),
Keenan Crane
(Carnegie Mellon University),
Bailin Deng
(University of Hull),
Sofien Bouaziz,
Daniel Piker,
Mark Pauly
(EPFL)
- Interactive Design of Developable Surfaces
(TOG Paper)
-
Chengcheng Tang
(King Abdullah University Of Science And Technology),
Pengbo Bo
(Harbin Institute of Technology at Weihai),
Johannes Wallner
(TU Graz),
Helmut Pottmann
(TU Wien)
- Computational Design of Reconfigurables
-
Akash Garg,
Alec Jacobson,
Eitan Grinspun
(Columbia University)
- Efficient and Flexible Deformation Representation for Data-Driven Surface Modeling
(TOG Paper)
-
Lin Gao
(Institute of Computing Technology, Chinese Academy of Sciences),
Yu-Kun Lai
(Cardiff University),
Dun Liang
(Tsinghua University, Beijing),
Shu-Yu Chen, Shihong Xia
(Institute of Computing Technology, Chinese Academy of Sciences),
- cSculpt: A System for Collaborative Sculpting
-
Claudio Calabrese,
Gabriele Salvati,
Marco Tarini
(ISTI/CNR and University of Insubria),
Fabio Pellacini
(Sapienza University of Rome)
- StyLit: Illumination-Guided Example-Based Stylization of 3D Renderings
-
Jakub Fiser,
Ondrej Jamriska,
Michal Lukac
(Czech Technical University in Prague),
Eli Shechtman,
Paul Asente,
Jingwan Lu
(Adobe Research),
Daniel Sykora
(Czech Technical University in Prague)
- Flow-Guided Warping for Image-Based Shape Manipulation
-
Romain Vergne
(Universite Grenoble Alpes and Inria),
Pascal Barla
(Inria),
Georges-Pierre Bonneau
(Universite Grenoble Alpes and Inria),
Roland W. Fleming
(Justus Liebig Universitat Giesen)
- Transfiguring Portraits
-
Ira Kemelmacher-Shlizerman
(University of Washington)
- Band-Sifting Decomposition for Image Based Material Editing
(TOG Paper)
-
Ivaylo Boyadzhiev,
Kavita Bala
(Cornell University),
Sylvain Paris
(Adobe Systems, Inc.),
Edward H. Adelson
(MIT)
- Interactive Acoustic Transfer Approximation for Modal Sound
(TOG Paper)
-
Dingzeyu Li,
Yun Fei,
Changxi Zheng
(Columbia University)
- Toward Animating Water with Complex Acoustic Bubbles
-
Timothy R. Langlois
(Cornell University),
Changxi Zheng
(Columbia University),
Doug James
(Stanford University)
- Generalized Non-reflecting Boundaries for Fluid Re-Simulation
-
Morten Bojsen-Hansen,
Chris Wojtan
(Institute of Science and Technology Austria)
- Preserving Geometry and Topology for Fluid Flows with Thin Obstacles and Narrow Gaps
-
Vinicius C. Azevedo
(Instituto de Informatica - UFRGS),
Christopher Batty
(University of Waterloo),
Manuel M. Oliveira
(Instituto de Informatica -- UFRGS)
- Synthesis of Filigrees for Digital Fabrication
-
Weikai Chen,
Xiaolong Zhang
(The University of Hong Kong),
Shiqing Xin
(Ningbo University),
Yang Xia
(Dalian University of Technology),
Sylvain Lefebvre
(INRIA),
Wenping Wang
(The University of Hong Kong)
- Designing Structurally Sound Ornamental Curve Networks
-
Jonas Zehnder
(Disney Research Zurich),
Stelian Coros
(Carnegie Mellon University),
Bernhard Thomaszewski
(Disney Research Zurich)
- Connected Fermat Spirals for Layered Fabrication
-
Haisen Zhao,
Fanglin Gu
(Shandong University),
Qixing Huang
(Toyota Technological Institute at Chicago),
J. A. Garcia Galicia
(Purdue University),
Yong Chen
(University of Southern California),
Changhe Tu
(Shandong University),
Bedrich Benes
(Purdue University),
Hao Zhang
(Simon Fraser University),
Daniel Cohen-Or
(Tel Aviv University),
Baoquan Chen
(Shandong University)
- Printing Arbitrary Meshes With a 5DOF Wireframe Printer
-
Rundong Wu,
Huaishu Peng,
Francois Guimbretiere,
Steve Marschner
(Cornell University)
- Live Intrinsic Video
-
Abhimitra Meka,
Michael Zollhoefer
(Max Planck Institute for Informatics),
Christian Richardt
(Max Planck Institute for Informatics and Intel Visual Computing Institute),
Christian Theobalt
(Max Planck Institute for Informatics)
- Multi-View Intrinsic Images of Outdoors Scenes with an Application to Relighting
(TOG Paper)
-
Sylvain Duchene,
Clement Riant,
Gaurav Chaurasia,
Jorge Lopez-Moreno,
Pierre-Yves Laffont,
Stefan Popov,
Adrien Bousseau,
George Drettakis
(INRIA, Sophia-Antipolis France)
- Let there be Color!: Joint End-to-end Learning of Global and Local Image Priors for Automatic Image Colorization with Simultaneous Classification
-
Satoshi Iizuka*,
Edgar Simo-Serra*,
Hiroshi Ishikawa
(Waseda University)
(*equal contribution)
- Approximate Translational Building Blocks for Image Decomposition and Synthesis
(TOG Paper)
-
Chuan Li,
Michael Wand
(Utrecht University)
- A Semi-Implicit Material Point Method for the Continuum Simulation of Granular Materials
-
Gilles Daviet,
Florence Bertails-Descoubes
(INRIA)
- Drucker-Prager Elastoplasticity for Sand Animation
-
Gergely Klar,
T. Gast, A. Pradhana, C. Fu
(University Of California, Los Angeles),
Craig Schroeder
(Walt Disney Animation Studios),
Chenfanfu Jiang,
Joseph Teran
(University Of California, Los Angeles)
- Non-Smooth Developable Geometry for Interactively Animating Paper Crumpling
(TOG Paper)
-
Camille Schreck
(Univ. Grenoble-Alpes / CNRS / INRIA),
Damien Rohmer
(Univ. Grenoble-Alpes / Lyon University / CNRS / CPE Lyon),
Stefanie Hahmann,
Marie-Paule Cani
(Univ. Grenoble-Alpes / CNRS / INRIA),
Shuo Jin,
Charlie C.L. Wang
(The Chinese University of Hong Kong),
Jean-Francis Bloch
(Univ. Grenoble-Alpes and CNRS)
- Fast Approximations for Boundary Element Based Brittle Fracture Simulation
-
David Hahn,
Chris Wojtan
(Institute of Science and Technology Austria (IST Austria))
- Bounded Distortion Harmonic Shape Interpolation
-
Edward Chien
(Bar-Ilan University),
Renjie Chen
(Max-Planck-Institut Informatik),
Ofir Weber
(Bar-Ilan University)
- On the Convexity and Feasibility of the Bounded Distortion Harmonic Mapping Problem
-
Zohar Levi
(Victoria University of Wellington),
Ofir Weber
(Bar-Ilan University)
- Volume-encoded UV-maps
-
Marco Tarini
(University of Insubria and ISTI/CNR)
- Motion Graphs for Unstructured Textured Meshes
-
Fabian Prada,
Michael Kazhdan
(The Johns Hopkins University),
Ming Chuang,
Alvaro Collet,
Hugues Hoppe
(Microsoft Research)
- Real-time Rendering on a Power Budget
-
Rui Wang,
Bowen Yu
(Zhejiang University),
Julio Marco
(Universidad de Zaragoza),
Tianlei Hu
(Zhejiang University),
Diego Gutierrez
(Universidad de Zaragoza),
Hujun Bao
(Zhejiang University)
- A System for Rapid Exploration of Shader Optimization Choices
-
Yong He
(Carnegie Mellon University),
Tim Foley
(NVIDIA Corporation),
Kayvon Fatahalian
(Carnegie Mellon University)
- Efficient GPU Rendering of Subdivision Surfaces using Adaptive Quadtrees
-
Wade Brainerd
(Activision),
Tim Foley,
Manuel Kraemer,
Henry Moreton
(NVIDIA Corporation),
Matthias Niesner
(Stanford University)
- Ebb: A DSL for Physical Simulation on CPUs and GPUs
-
Gilbert Bernstein,
Chinmayee Shah,
Crystal Lemire,
Zachery DeVito,
Matthew Fisher,
Philip Levis,
Pat Hanrahan
(Stanford University)
- Simit: A Language for Physical Simulation
(TOG Paper)
-
Fredrik Kjolstad
(MIT CSAIL),
Shoaib Kamil
(Adobe),
Jonathan Ragan-Kelley
(Stanford University),
David Levin
(Disney Research),
Shinjiro Sueda
(California Polytechnic State University),
Desai Chen
(MIT CSAIL),
Etienne Vouga
(University of Texas at Austin),
Danny Kaufman
(Adobe),
Gurtej Kanwar,
Wojciech Matusik,
Saman Amarasinghe
(MIT CSAIL)
- Parallel Inverse Kinematics for Multithreaded Architectures
(TOG Paper)
-
Pawan Harish,
Mentar Mahmudi
(EPFL),
Benoit Le Callennec
(Moka Studio),
Ronan Boulic
(EPFL)
- Fusion4D: Real-time Performance Capture of Challenging Scenes
-
M. Dou,
S. Khamis,
Y. Degtyarev,
P. Davidson,
Sean Ryan Fanello,
A. Kowdle,
S. Orts Escolano,
C. Rhemann,
(Istituto Italiano di Tecnologia),
David Kim,
David Sweeney,
J. Taylor,
Pushmeet Kohli,
V. Tankovich,
Shahram Izadi
(Microsoft Research Cambridge)
- An Anatomically Constrained Local Deformation Model for Monocular Face Capture
-
Chenglei Wu
(ETH Zurich),
Derek Bradley
(Disney Research Zurich),
Markus Gross
(Disney Research Zurich and ETH Zurich),
Thabo Beeler
(Disney Research Zurich)
- AutoHair: Fully Automatic Hair Modeling from A Single Image
-
Menglei Chai,
Tianjia Shao,
Hongzhi Wu,
Yanlin Weng,
Kun Zhou
(Zhejiang University)
- Lightweight Eye Capture Using a Parametric Model
-
Pascal Berard
(ETH Zurich and Disney Research Zurich),
Derek Bradley
(Disney Research Zurich),
Markus Gross
(ETH Zurich and Disney Research Zurich),
Thabo Beeler
(Disney Research Zurich)
- Reconstruction of Personalized 3D Face Rigs from Monocular Video
(TOG Paper)
-
Pablo Garrido,
Michael Zollhoefer,
Dan Casas,
Levi Valgaerts
(MPI for Informatics),
Kiran Varanasi,
Patrick Perez
(Technicolor),
Christian Theobalt
(MPI for Informatics)
- Realtime 3D Eye Gaze Animation Using a Single RGB Camera
-
Congyi Wang
(Institute of Computing Technology, Chinese Academy of Sciences),
Fuhao Shi
(Texas A&M University),
Shihong Xia
(Institute of Computing Technology, Chinese Academy of Sciences),
Jinxiang Chai
(Texas A&M University),
- The Sketchy Database: Learning to Retrieve Badly Drawn Bunnies
-
Patsorn Sangkloy
(Georgia Institute of Technology),
Nathan Burnell
(Brown University),
Cusuh Ham,
James Hays
(Georgia Institute of Technology)
- Fidelity vs. Simplicity : a Global Approach to Line Drawing Vectorization
-
Jean-Dominique Favreau,
Florent Lafarge,
Adrien Bousseau
(INRIA)
- Learning to Simplify: Fully Convolutional Networks for Rough Sketch Cleanup
-
Edgar Simo-Serra*,
Satoshi Iizuka*,
Kazuma Sasaki,
Hiroshi Ishikawa
(Waseda University)
(*equal contribution)
- Legible Compact Calligrams
-
Changqing Zou,
(Simon Fraser University),
Junjie Cao
(Dalian University of Technology),
Warunika Ranaweera,
Ibraheem Alhashim,
Ping Tan
(Simon Fraser University),
Alla Sheffer
(University of British Columbia),
Hao Zhang
(Simon Fraser University)
- My Text in Your Handwriting
(TOG Paper)
-
Tom S.F. Haines,
Oisin Mac Aodha,
Gabriel J. Brostow
(University College London)
- HexEx: Robust Hexahedral Mesh Extraction
-
Max Lyon,
David Bommes,
Leif Kobbelt
(RWTH Aachen University)
- All-Hex Meshing Using Closed Form-Induced Polycubes
-
Xianzhong Fang,
Weiwei Xu,
Hujun Bao,
Jin Huang
(Zhejiang University)
- Fast and Exact Discrete Geodesic Computation Based on Triangle-Oriented Wavefront Propagation
-
Yipeng Qin*
(Bournemouth University),
Xiaoguang Han*
(The University of Hong Kong),
Hongchuan Yu
(Bournemouth University),
Yizhou Yu
(The University of Hong Kong),
Jianjun Zhang
(Bournemouth University)
(* Joint first authors)
- Interactively cutting and constraining nodes in a mesh by augmented matrices,
(TOG Paper)
-
Yu-Hong Yeung,
Jessica Crouch,
Alex Pothen
(Purdue University)
- Real-time Facial Animation with Image-based Dynamic Avatars
-
Chen Cao,
Hongzhi Wu,
Yanlin Weng,
Tianjia Shao,
Kun Zhou
(Zhejiang University)
- JALI: An Animator-Centric Viseme Model for Expressive Lip Synchronization
-
Pif Edwards,
Chris Landreth,
Eugene Fiume
Karan Singh
(University of Toronto)
- Perspective-aware Manipulation of Portrait Photos
-
Ohad Fried
(Princeton University),
Eli Shechtman
(Adobe Research),
Dan Goldman
(Google),
Adam Finkelstein
(Princeton University)
- Painting Style Transfer for Head Portraits Using Convolutional Neural Networks
-
Ahmed Selim
(Trinity College Dublin),
Mohamed Elgharib
(Qatar Computing Research Institute),
Linda Doyle
(Trinity College Dublin)
- Interactive Sketching of Urban Procedural Models
-
Gen Nishida,
Ignacio Garcia-Dorado
(Purdue University),
Adrien Bousseau
(INRIA),
Bedrich Benes,
Daniel G. Aliaga
(Purdue University)
- Computational Network Design from Functional Specifications
-
Chi-Han Peng
(University College London),
Yong-Liang Yang
(University of Bath),
Fan Bao,
Daniel Fink
(University College London),
Dong-Ming Yan
(KAUST),
Peter Wonka
(Arizona State University),
Niloy Mitra
(University College London)
- Crowd-driven Mid-scale Layout Design
-
Tian Feng
(Singapore University of Technology and Design),
Lap-Fai (Craig) Yu
(University of Massachusetts Boston),
Sai-Kit Yeung
(Singapore University of Technology and Design),
KangKang Yin
(National University of Singapore),
Kun Zhou
(Zhejiang University)
- A Probabilistic Model for Exteriors of Residential Buildings
-
Lubin Fan
(King Abdullah University of Science and Technology),
Peter Wonka
(Arizona State University)
- Discrete Connection and Covariant Derivative for Vector Field Analysis and Design
(TOG Paper)
-
Beibei Liu,
Yiying Tong
(Michigan State University),
Fernando de Goes,
Mathieu Desbrun
(California Institute of Technology)
- Subdivision Exterior Calculus for Geometry Processing
-
Fernando de Goes
(Pixar Animation Studios),
Mathieu Desbrun
(California Institute of Technology),
Mark Meyer,
Tony DeRose
(Pixar Animation Studios)
- Accelerated Quadratic Proxy for Geometric Optimization
-
Shahar Kovalsky,
Meirav Galun,
Yaron Lipman
(Weizmann Institute of Science)
- G1 Non-Uniform Catmull-Clark Surfaces
-
Xin Li
(University of Science and Technology of China),
G. Thomas Finnigan
(Autodesk, Inc.),
Thomas Sederberg
(Brigham Young University)
- Construction of Manifolds via Compatible Sparse Representations
(TOG Paper)
-
Ruimin Wang,
Ligang Liu,
Zhouwang Yang,
Kang Wang,
Wen Shan,
Jiansong Deng,
Falai Chen
(University of Science and Technology of China)
- Intrinsic Girth Function for Shape Processing
(TOG Paper)
-
Shiqing Xin
(Ningbo University),
Wenping Wang
(The University of Hong Kong)
Shuangmin Chen,
Jieyu Zhao
(Ningbo University),
Zhenyu Shu
(Ningbo University and Zhejiang University)
- SemanticPaint: Interactive 3D Labeling and Learning at your Fingertips
(TOG Paper)
-
Julien Valentin,
Vibhav Vineet
(University of Oxford),
Ming-Ming Cheng
(University of Oxford and Nankai University),
David Kim,
Jamie Shotton,
Pushmeet Kohli
(Microsoft Research Cambridge),
Matthias Niesner
(Stanford University),
Antonio Criminisi,
Shahram Izadi
(Microsoft Research Cambridge),
Philip Torr
(University of Oxford)
- Detail-preserving Mesh Unfolding for Non-rigid Shape Retrieval
(TOG Paper)
-
Yusuf Sahillioglu
(Middle East Technical University),
Ladislav Kavan
(University of Utah)
- Modeling Dense Inflorescences
-
Andrew Owens,
Mikolaj Cieslak,
Mikolaj Cieslak
(University of Calgary),
Regine Classen-Bockhoff
(Johannes Gutenberg-Universitat Mainz),
Przemyslaw Prusinkiewicz
(University of Calgary)
- Spectral Style Transfer for Human Motion between Independent Actions
-
M. Ersin Yumer
(Adobe Research),
Niloy Mitra
(University College London)
- A Deep Learning Framework for Character Motion Synthesis and Editing
-
Daniel Holden
(The University of Edinburgh),
Jun Saito
(Marza Animation Planet Inc.),
Taku Komura
(The University of Edinburgh)
- PiGraphs: Learning Interaction Snapshots from Observations
-
Manolis Savva,
Angel X. Chang,
Pat Hanrahan,
Matthew Fisher,
Matthias Niesner
(Stanford University)
- Multi-Scale Label-Map Extraction for Texture Synthesis
-
Yitzchak Lockerman
(Yale University),
Basile Sauvage,
Remi Allegre,
Jean-Michel Dischler
(ICube - University of Strasbourg),
Julie Dorsey,
Holly Rushmeier
(Yale University)
- Time-varying Weathering in Texture Space
-
Rachele Bellini,
Yanir Kleiman,
Daniel Cohen-Or
(Tel Aviv University)
- Vector Regression Functions for Texture Compression
(TOG Paper)
-
Ying Song
(Chinese Academy of Sciences),
Jiaping Wang
(Aiur),
Li-Yi Wei
(Dragoniac),
Wencheng Wang
(Chinese Academy of Sciences)
- Multiway K-Clustered Tensor Approximation: Toward High- Performance Photorealistic Data-Driven Rendering
(TOG Paper)
-
Yu-Ting Tsai
(Yuan Ze University, Taiwan)
- DisCo: Display-Camera Communication Using Rolling Shutter Sensors
-
Kensei Jo
(Sony Corporation),
Mohit Gupta
(University of Wisconsin - Madison),
Shree K. Nayar
(Columbia University)
- Soli: Ubiquitous Gesture Sensing With Millimeter Wave Radar
-
Mustafa Karagozler,
Ivan Poupyrev,
Jaime Lien,
Carsten Schwesig,
Nick Gillian,
Patrick Amihood,
Hakim Raja,
Erik Olson
(Google Inc.)
- Efficient and Precise Interactive Hand Tracking through Joint, Continuous Optimization of Pose and Correspondences
-
Jonathan Taylor,
Lucas Bordeaux,
Thomas Cashman,
Bob Corish,
Cem Keskin,
Toby Sharp
(Microsoft Research),
Eduardo Soto
(Microsoft Research and McMaster University),
David Sweeney
(Microsoft Research),
Julien Valentin
(Oxford University and Microsoft Research),
Benjamin Luff
(Abertay University and Microsoft Research),
Arran Topalian
(Microsoft Research and Abertay University),
Erroll Wood
(University of Cambridge and Microsoft Research),
Sameh Khamis,
Pushmeet Kohli,
Shahram Izadi,
Richard Banks,
Andrew Fitzgibbon,
Jamie Shotton
(Microsoft Research)
- Rig Animation with a Tangible and Modular Input Device
-
Olivier Glauser,
Wan-Chun Ma
(ETH Zurich),
Daniele Panozzo
(ETH Zurich and New York University),
Alec Jacobson
(Columbia University),
Otmar Hilliges,
Olga Sorkine-Hornung
(ETH Zurich)
- Artist-Directed Dynamics for 2D Animation
-
Yunfei Bai
(Adobe Research and Georgia Institute of Technology),
Danny Kaufman
(Adobe Research),
C. Karen Liu
(Georgia Institute of Technology),
Jovan Popovic
(Adobe Research)
- Modeling Character Canvases from Cartoon Drawings
(TOG Paper)
-
Mikhail Bessmeltsev,
William Chang,
Nicholas Vining,
Alla Sheffer
(University of British Columbia),
Karan Singh
(University of Toronto)
- SketchiMo: Sketch-based Motion Editing for Articulated Characters
-
Byungkuk Choi*, Roger Blanco i Ribera*, J. P. Lewis, Yeongho Seol, Seokpyo Hong,
Haegwang Eom, Sunjin Jung,
Junyong Noh
(KAIST)
(*equal contribution)
- Shadow Theatre: Discovering Human Motion from a Sequence of Silhouettes
-
Jungdam Won,
Jehee Lee
(Seoul National University)
- Automatic Triage for a Photo Series
-
Huiwen Chang,
Fisher Yu
(Princeton University),
Jue Wang
(Adobe Research),
Douglas Ashley,
Adam Finkelstein
(Princeton University)
- Automatic Photo Adjustment Using Deep Neural Networks
(TOG Paper)
-
Zhicheng Yan
(University of Illinois at Urbana-Champaign),
Hao Zhang
(Carnegie Mellon University),
Baoyuan Wang
(Microsoft Research),
Sylvain Paris
(Adobe Systems, Inc.),
Yizhou Yu
(The University of Hong Kong)
- EyeOpener: Editing Eyes in the Wild
-
Zhixin Shu
(Stony Brook University),
Eli Shechtman
(Adobe Research),
Dimitris Samaras,
Sunil Hadap
(Adobe Research)
- Sky is Not the Limit
-
Yi-Hsuan Tsai
(University of California, Merced),
Xiaohui Shen,
Zhe Lin,
Kalyan Sunkavalli
(Adobe Research),
Ming-Hsuan Yang
(University of California, Merced)
kesen.huang@gmail.com


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re-generate "index-compiled.js" via "npm run commonjs2-import-example"
"npm run commonjs2-import-example" is defined in package.json scripts.commonjs2-import-example
Browserify is used by this script to compile a commonjs2 module
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the and Nsp The with helicase for were protein are residues structure SARS-CoV- was al., binding which using Fig. proteins inhibitors model {background silico structures coronavirus energy enzyme template (Suppl. drugs target domain margin that âˆ’. found indicating {border-style approved both between px;} respectively such #ffffff;} interactions number Table from its pocket score sequence FDA against antiviral respiratory Ala, Arg, Asn, acid amino further syndrome viral Phe, SARS-CoV activity has homology nsp present severe this Image acute modelling molecular âˆ’.Â â€" Asp, Gln, Lys, N-MTase Tyr, docking drug hydrophobic identified lead models potential respectively. these two used Gly, Grazoprevir Paritaprevir RNA hydrogen query screening site their version âˆ’,. â€² Simeprevir Thr, control five inhibition molecules most values Î¼M (GZR) (PTV) Structure Trp, along comparative dual have inhibitor kcal/mol lowest parameters px; replication represented residue structure-based value within {font-family Biol. Leu, NTPase Pro, Val, alignment approach bonds can constant font-size inhibitors. ligand secondary total (.%) Arg COVID- DOPE GZR Ser, active atoms been colour data different domains double; drugs. grey grid human identity index interaction into key leads molecule not predicted proteins. results sequences similar study three-dimensional through top transmembrane virtual whereas (Table A). Both Cys ERRAT Elsevier Ile, MOLPDF Molecular PTV SARS SMV Scutellarin Sinefungin These acids aliphatic all analogues available based border-color border-width calculated case charged coloured complex conformation coverage cycle discovery disease elements essential experimental green helices host information knowledge-based low novel optimized pairs properties quality rate region regions research scores show solid; spheres studies. three unwinding where {font-style {font-weight (A) (B) (C) (For (Jia (SMV) (SMV), (light B). Cys, Câ€"D). DNA Gly H-bonds MODELLER Model PDB Phe Ramachandran Ser Virol. Z-score analysis any article.) atom average basic best bold caused centre compounds consists courier; curve database deviation each em; exhibits few figure highly hydropathicity identification important infections inhibit interpretation legend, letters life ligand-binding line nearest non-structural nucleic pink plot point potent proteinase pt;} reader red references referred reported residues- resource respective selected set showing shows side stabilized strict structural studies study, superfamily transcription virus web Ã—Â Î¼M,


FoxTrot
FQL
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alwaysVisible attribute disables panel hiding in a screen when it is opened as lookup. If the attribute value is true, the buttons panel is not hidden. By default, the attribute value is false.
alwaysVisible
true
false


According to Prothom Alo sports editor Sadman Sabah Ali, Shakib "was fairly proficient at cricket and was often hired to play for different villages".In one of these matches Shakib impressed an umpire who arranged for him to practice with the Islampur Para Club, a team in the Magura Cricket League. He was picked to play for Islampur and took a wicket with his first ball; it was his first delivery with a proper cricket ball, having previously generally played with a taped tennis ball. He spent six months training at Bangladesh Krira Shiksha Pratisthan, a government-run sports institute. In 2005, during the final of a tri-nation tournament involving England and Sri Lanka's Under-19 teams, Shakib scored an 86-ball century and took three wickets to lead his team to victory. Between 2005 and 2006, Shakib played 18 youth One Day Internationals; he scored 563 runs at an average of 35.18, and took 22 wickets at an average of 20.18.[10].
Shakib made his One-Day International debut against Zimbabwe in August 2006 primarily as a bowler, but over time started concentrating on his batting. However his bowling talents were re-discovered in the 2008 home Test series against New Zealand where in an exceptional first game, he ran through the Kiwi line-up with figures of 7-37 in the first innings and a fluent 71 in the second innings also reiterated his batting skills. Gradually, he became the undisputed all-rounder in the side. His growth as a player saw him being elevated to vice captaincy in 2009 but Shakib showed he was up for captaincy when as a stand-in captain on the fifth day of a West Indies Test in 2009, he led Bangladesh to their first ever victory on foreign soil. Mashrafe Mortaza's injury saw Shakib getting a prolonged shot at captaincy and though his stints as captain had its highs and lows, he continued to come through leaps and bounds as a player. But in mid-2010, he decided to give up captaincy. However, fate had other plans as Mortaza once again fell prey to injury and Shakib was asked to lead the side in the home ODI series against New Zealand in which Bangladesh thrashed the Kiwis by a whopping 4-0 margin. The following year, Bangladesh toured Zimbabwe to play a one-off Test and five ODIs. Under skipper Shakib, the team put up a disastrous performance . Immediate repercussions of the defeat saw Shakib being axed by the Bangladesh Cricket Board.
The all-rounder was signed by the Kolkata Knight Riders in the Indian Premier League for the 2011 season and was retained the following year and did well enough to be bought back in 2014 for the 7th season. In 2015 after batting in the first match at 2015 Cricket World Cup, Hasan had a total of 4,040 runs in ODI matches and became the first Bangladeshi cricketer to score 4,000 runs in ODIs.
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Shakib Al Hasan , a talented left-hand batsman and left-arm spinner, is unarguably the best cricketer that Bangladesh have ever had.


Invoke Dynamic (Also known as Indy) was part of JSR 292 intended to enhance the JVM support for dynamically typed languages.
After its first release in Java 7, the invokedynamic
opcode is used quite extensively by dynamic JVM-based languages like JRuby and even statically typed languages like Java.
Before Java 7, the JVM only had four method invocation types:
invokevirtual
to call normal class methods,
invokestatic
to call static methods,
invokeinterface
to call interface methods,
and invokespecial
to call constructors or private methods.
Despite their differences, all these invocations share one simple trait: They have a few predefined steps to complete each method call, and we can't enrich these steps with our custom behaviors.
There are two main workarounds for this limitation: One at compile-time and the other at runtime. The former is usually used by languages like Scala or Koltin and the latter is the solution of choice for JVM-based dynamic languages like JRuby.
The runtime approach is usually reflection-based and consequently, inefficient.
On the other hand, the compile-time solution is usually relying on code-generation at compile-time. This approach is more efficient at runtime. However, it's somewhat brittle and also may cause a slower startup time as there's more bytecode to process.
Now that we've got a better understanding of the problem, let's see how the solution works internally.


| Documentation Contents |
jstack [ option ] pid jstack [ option ] executable core jstack [ option ] [server-id@]remote-hostname-or-IP
Options are mutually exclusive. Option, if used, should follow immediately after the command name. See OPTIONS.
- pid
- process id for which the stack trace is to be printed. The process must be a Java process. To get a list of Java processes running on a machine, jps may be used.
- executable
- Java executable from which the core dump was produced.
- core
- core file for which the stack trace is to be printed.
- remote-hostname-or-IP
- remote debug server's (see jsadebugd) hostname or IP address.
- server-id
- optional unique id, if multiple debug servers are running on the same remote host.
jstack prints Java stack traces of Java threads for a given Java process or core file or a remote debug server. For each Java frame, the full class name, method name, 'bci' (byte code index) and line number, if available, are printed. With the -m option, jstack prints both Java and native frames of all threads along with the 'pc' (program counter). For each native frame, the closest native symbol to 'pc', if available, is printed. C++ mangled names are not demangled. To demangle C++ names, the output of this command may be piped to c++filt. If the given process is running on a 64-bit VM, you may need to specify the -J-d64 option, e.g.:
jstack -J-d64 -m pidNOTE - This utility is unsupported and may or may not be available in future versions of the JDK. In Windows Systems where dbgent.dll is not present, 'Debugging Tools for Windows' needs to be installed to have these tools working. Also, the PATH environment variable should contain the location of jvm.dll used by the target process or the location from which the Crash Dump file was produced.
For example, set PATH=<jdk>\jre\bin\client;%PATH%
- -F
- Force a stack dump when 'jstack [-l] pid' does not respond.
- -l
- Long listing. Prints additional information about locks such as list of owned java.util.concurrent ownable synchronizers.
- -m
- prints mixed mode (both Java and native C/C++ frames) stack trace.
- -h
- prints a help message.
- -help
- prints a help message
Mixed mode stack trace, the -m option, does not work with the remote debug server.
Copyright © 1993, 2011, Oracle and/or its affiliates. All rights reserved. Please send comments using this Feedback page. |
Java Technology |


|
CryptoAuthLib
Atmel CryptoAuthentication Library
|
This code base implements an object-oriented C library which supports Atmel CryptoAuth devices. The family of devices supported currently are:
Prerequisite skills:
Prerequisite hardware to run CryptoAuthLib examples:
For most development, using socketed top-boards is preferable until your configuration is well tested, then you can commit it to a CryptoAuth Xplained Pro Extension, for example. Keep in mind that once you lock a device, it will not be changeable.
01/08/2016
9/19/2015
CryptoAuthLib will run on a variety of platforms from small micro-controllers to desktop host systems. The current list of hardware abstraction layer support includes:
Rich OS Hosts:
Microcontrollers:
If you have specific microcontrollers or Rich OS platforms you need support for, please contact us through the Atmel support portal with your request.
See the 'docs' directory of CryptoAuthLib for supporting documentation including architecture diagrams and more detailed usage docs.
The library is structured to support portability to:
All platform dependencies are contained within the HAL (hardware abstraction layer).
There are three primary object types in CryptoAuthLib:
ATCADevice is a composite object made up of ATCACommand ATCAIface.
Currently, the vast majority of testing has been performed on:
These host containers implement a host test environment and test console to exercise tests. They presume that a CryptoAuth Xplained Pro or other I2C socket for an ATECC508A are connected to the I2C pins of the host Xplained Pro development board or in the case of windows is using a HID connection to an ATCK101 or ATCK590.
The unit tests and basic tests exercise the core datasheet commands of the device as well as the more convenient, basic API methods.
If you need an example of how to use a command, these hosts and tests are a good place to reference.
Even though this is a C library, it follows object-oriented design patterns.
An object is minimally defined to be data and the actions which operate on that data.
Each CryptoAuth device is a composite object, a structure which includes the command table (list of commands) which are valid for the device, and the data used to hold the state of that device.
ATCADevice is the object which represents the Atmel CryptAuth device
ATCACommand is the object which represents the valid methods of the Device.
ATCAInterface is the physical interface object (I2C or SWI instance). Currently, each Device may have a single OATCAInterface.
ATCADevice represents an ATSHA or ATECC family device.
In order to add new protocol support for a platform, you provide a HAL (hardware abstraction layer) C file for the protocol and target. In your project's IDE or Makefile, you select which HAL support you need for the hardware configuration. Generally, there are separate files for each protocol and platform combination - (ie: samd21_i2c_asf.c would target SAMD21 MCUs with I2C using the ASF low-level driver support.)
There is a set of unit tests found in the test directory which will at least partially demonstrate the use of the objects. Some tests may depend upon a certain device being configured in a certain way and may not work for all devices or specific configurations of the device.
Using a new library is often easier when you can load an example and see how it works. We've provided examples in the form of "host containers" which are host projects that incorporate CryptoAuthLib and target various processors or communication APIs.
We maintain host test containers for each of the HAL layers we support. We've published the host container for SAMD21 which demonstrates a simple console interface to invoke test runners.
Look for:
at the Atmel CryptoAuthentication web pages or in GitHub.
The best way to learn how to use CryptoAuthLib is to study the host test projects that exercise the library and ATECC and ATSHA devices.
New examples will be forthcoming as the software matures. Continue checking the CryptoAuthentication web page for new updates.
You can include this project in your own project under git.
Using CryptoAuthLib as a git submodule, you can maintain your application separately from CryptoAuthLib.
If your project is already in in git but you haven't yet intergrated CryptoAuthLib, change to the directory where you want to put CryptoAuthLib
This adds CryptoAuthLib as a subdirectory and separate git repo within your own project. Changes and commits to your project vs CryptoAuthLib will remain separated into each respective repository.
If there is a project you want to checkout that already incorporates CryptoAuthLib as a submodule if you clone the repo that incorporates CryptoAuthLib, after cloning, you'll still need to fill out the CryptoAuthLib submodule after cloning:
Now that CryptoAuthLib is a full-fledged submodule in your git project, in order to easily add it to your project within Atmel Studio, please see this tip
1) In your Makefile or IDE, choose the HAL support you need from the HAL directory and exclude other HAL files from your project.
2) For I2C interfaces, define the symbol ATCA_HAL_I2C in your compiler's symbol definitions. This will hook up the CryptoAuthLib interface class with your HAL implementation of I2C methods.
3) HAL implementations for CDC and HID interfaces to the ATCK101 are also included for use with Windows or Linux versions of the test host.


I like to cook my lentils in my cast iron frying pan that my grandmother bequeathed to me.
In a warm pan I add some red lentils, maybe 1/2 cup.
Then I add about a cup to a cup and a half of water, depends on how thick I want my soup on that day.
Then I thrown in a bouillon cube, vegetable since I am vegetarian, and cover the pan with a lid. I leave it on low-medium heat and go back to coding.
When the lentils have been simmering a while, maybe 10 minutes, I add about 1/2 to 3/4 of a cup of frozen chopped spinach. I put it in frozen, it will melt in the pan. I also sometimes add one or two frozen plum tomatoes from my father's crop last summer, and replace the lid and let it continue to simmer. Back to coding.
I don't leave the lid on too tight with the lentils on too high or the lentils may boil over the side. Messy.
Once I have hit enough of a wall coding that it is time for a break, I season the lentils with cumin and tranfer to a bowl. I sometimes add a spoonful or two (1/2 a cup?) of yogurt or quark (a kind of fresh cheese) to cool it a bit and lend a creamy texture.
Hope this works for you and feel free to Tweet with questions or success stories. Cheers, Anita.


Hello, Stranger!
Welcomer to Runners Home™
Runners Home™ is dedicated to providing you with:
the most up-to-the date information on running races
the best resurses for runners
© Runners Home. All rigth rezeved.


Before Change
max_free_energy = free_energies.max()
f_reduced = free_energies - max_free_energy
logZ = max_free_energy + f_reduced.exp().sum().log()
return logZ.exp()
def probability(nn_state, v, Z):
After Change
// f_reduced = free_energies - max_free_energy
// logZ = max_free_energy + f_reduced.exp().sum().log()
// return logZ.exp()
return torch.tensor(
nn_state.rbm_am.compute_partition_function(visible_space),
device=nn_state.device, dtype=torch.double
)
def probability(nn_state, v, Z):
Evaluates the probability of the given vector(s) of visible


Not referred floor price issue to Trai: DoT source
The DoT source also said Digital Communications Commission (DCC) will meet in the first half of December and could take up the issue of spectrum auction pricing.
by PTIThe telecom department has not referred the issue of fixing floor price for telecom tariffs to Trai, according to a source.
The DoT source also said Digital Communications Commission (DCC) will meet in the first half of December and could take up the issue of spectrum auction pricing.
Asked if Department of Telecom (DoT) will make any reference to Telecom Regulatory Authority of India (Trai) over floor price for tariffs, the source said, "We don't have any such plans".
The source also indicated that the telecom department will not intervene in recent announcement by telecom operators to raise tariffs.


bind - bind a name to a socket
#include <sys/types.h> /* See NOTES */
#include <sys/socket.h>
int bind(int sockfd, const struct sockaddr *addr,
socklen_t addrlen);
When a socket is created with socket(2), it exists in a name space
(address family) but has no address assigned to it. bind() assigns the
address specified by addr to the socket referred to by the file
descriptor sockfd. addrlen specifies the size, in bytes, of the
address structure pointed to by addr. Traditionally, this operation is
called “assigning a name to a socket”.
It is normally necessary to assign a local address using
bind() before a SOCK_STREAM socket may receive connections
(see accept(2)).
The rules used in name binding vary between address families.
Consult the manual entries in Section 7 for detailed information. For
AF_INET, see ip(7); for AF_INET6, see ipv6(7);
for AF_UNIX, see unix(7); for AF_APPLETALK, see
ddp(7); for AF_PACKET, see packet(7); for
AF_X25, see x25(7); and for AF_NETLINK, see
netlink(7).
The actual structure passed for the addr argument will
depend on the address family. The sockaddr structure is defined as
something like:
struct sockaddr {
sa_family_t sa_family;
char sa_data[14];
}
The only purpose of this structure is to cast the structure
pointer passed in addr in order to avoid compiler warnings. See
EXAMPLE below.
On success, zero is returned. On error, -1 is returned, and errno is set
appropriately.
- EACCES
- The address is protected, and the user is not the superuser.
- EADDRINUSE
- The given address is already in use.
- EADDRINUSE
- (Internet domain sockets) The port number was specified as zero in the
socket address structure, but, upon attempting to bind to an ephemeral
port, it was determined that all port numbers in the ephemeral port range
are currently in use. See the discussion of
/proc/sys/net/ipv4/ip_local_port_range ip(7).
- EBADF
- sockfd is not a valid file descriptor.
- EINVAL
- The socket is already bound to an address.
- EINVAL
- addrlen is wrong, or addr is not a valid address for this
socket's domain.
- ENOTSOCK
- The file descriptor sockfd does not refer to a socket.
The following errors are specific to UNIX domain (AF_UNIX)
sockets:
- EACCES
- Search permission is denied on a component of the path prefix. (See also
path_resolution(7).)
- EADDRNOTAVAIL
- A nonexistent interface was requested or the requested address was not
local.
- EFAULT
- addr points outside the user's accessible address space.
- ELOOP
- Too many symbolic links were encountered in resolving addr.
- ENAMETOOLONG
- addr is too long.
- ENOENT
- A component in the directory prefix of the socket pathname does not
exist.
- ENOMEM
- Insufficient kernel memory was available.
- ENOTDIR
- A component of the path prefix is not a directory.
- EROFS
- The socket inode would reside on a read-only filesystem.
POSIX.1-2001, POSIX.1-2008, SVr4, 4.4BSD (bind() first appeared in
4.2BSD).
POSIX.1 does not require the inclusion of <sys/types.h>, and this
header file is not required on Linux. However, some historical (BSD)
implementations required this header file, and portable applications are
probably wise to include it.
For background on the socklen_t type, see
accept(2).
The transparent proxy options are not described.
An example of the use of bind() with Internet domain sockets can be found
in getaddrinfo(3).
The following example shows how to bind a stream socket in the
UNIX (AF_UNIX) domain, and accept connections:
#include <sys/socket.h>
#include <sys/un.h>
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#define MY_SOCK_PATH "/somepath"
#define LISTEN_BACKLOG 50
#define handle_error(msg) \
do { perror(msg); exit(EXIT_FAILURE); } while (0)
int
main(int argc, char *argv[])
{
int sfd, cfd;
struct sockaddr_un my_addr, peer_addr;
socklen_t peer_addr_size;
sfd = socket(AF_UNIX, SOCK_STREAM, 0);
if (sfd == -1)
handle_error("socket");
memset(&my_addr, 0, sizeof(struct sockaddr_un));
/* Clear structure */
my_addr.sun_family = AF_UNIX;
strncpy(my_addr.sun_path, MY_SOCK_PATH,
sizeof(my_addr.sun_path) - 1);
if (bind(sfd, (struct sockaddr *) &my_addr,
sizeof(struct sockaddr_un)) == -1)
handle_error("bind");
if (listen(sfd, LISTEN_BACKLOG) == -1)
handle_error("listen");
/* Now we can accept incoming connections one
at a time using accept(2) */
peer_addr_size = sizeof(struct sockaddr_un);
cfd = accept(sfd, (struct sockaddr *) &peer_addr,
&peer_addr_size);
if (cfd == -1)
handle_error("accept");
/* Code to deal with incoming connection(s)... */
/* When no longer required, the socket pathname, MY_SOCK_PATH
should be deleted using unlink(2) or remove(3) */
}
accept(2), connect(2), getsockname(2), listen(2),
socket(2), getaddrinfo(3), getifaddrs(3), ip(7),
ipv6(7), path_resolution(7), socket(7), unix(7)
This page is part of release 5.05 of the Linux man-pages project. A
description of the project, information about reporting bugs, and the latest
version of this page, can be found at https://www.kernel.org/doc/man-pages/.


Hamza Qureshi is a first-year Bachelor of Architecture student at Carnegie Mellon University.
ContinueQureshi's affinity for architecture began at age 12, when over the course of a year, he created an entire city on Google SketchUp. Seven years later, four of which were spent taking studio classes at the Academy of Architecture and Contemporary Themes in North Bergen, New Jersey, he has assembled a skillset and portfolio that explores analog, digital, and hybrid media.
See his skills!With over four years of studio experience, Qureshi can efficiently devise spatial schemes.
Qureshi is trained in a variety of analog techniques, including drafting, perspective drawing, and model-making
Qureshi has mastery-level skills in the Adobe Creative Suite and Rhinoceros, as well as knowledge of AutoCAD, 3ds Max, SketchUp, and Revit.
With years of experience, Qureshi has developed a passion for architecture and design.
Feel free to contact me for more information, references, or a detailed resume!
(551) 208-0399


def rules():
return [
UnionRule(ReplImplementation, PythonRepl),
run_python_repl,
]
def rules():
return [
subsystem_rule(IPython),
UnionRule(ReplImplementation, PythonRepl),
UnionRule(ReplImplementation, IPythonRepl),
run_python_repl,
run_ipython_repl,
]


Place a call by entering a phone numeber and a delay time in seconds. You can replay a call by clicking the replay button. This is only available once the call has been recieved and user has entered a number. Delete a call record by pressing the delete button.


The way to come up, adverts in thoughts
So just like any other personality of a business like staff, someone would have to make advert proving that they are good in the particular act
so here in the IT WORLD we realy do things the say way though computerised a bit, like web adds, hoope you have seen add on sites, thats one way
of pushing your advert to people, everyone in the web related course can do this, just take your self in the moods of coding and you will experience the margic,
more ways of advert are available though the most simple and easy to reach people is the online one, so things like posters on the road having maybe shafic phone
services or tonny web developers, these are nice provided they portay the works in which the poster stands so here people can have you in mind according to the info on the poster.


About Me
I am Veliana Georgieva, currently enrolled in the Year Up program specializing in computer coding. My goal is to acquire a job in the tech field such as a program manager. I have obtained my AA with emphasis in business from Highline College. Alongside this, I have a 3-year background in banking and management. This has led me to find the missing piece and decided to switch my career path to pursue my passion for coding. I am a self-motivated individual that has obtained amazing interpersonal and leadership skills with the ability to speak 2 languages, and currently learning multiply different kinds of coding languages. To sum it up, I am a qualified immigrant women with an abundance of professional skills and aspirations that is looking to take on the tech field by storm.


Fellows and the wider CfA community will discuss stories around civic engagement, what it's like to work in City Hall, and other key insights into the civic tech movement.
Join us as we demo projects, talk about opportunities and challenges and give the fellows feedback to help inform the year going forward.
Following the presentations will be a reception, and a chance to network with the CfA community.
We'd love to have you join us. Please select a free ticket below to sign up.
4:00 - 9:00 PM
Care to join us for the presentations and the after party?
7:00 - 9:00 PM
Are you the fashionably late type? That's cool, come hang with us.
We're still working to make this event totally awesome. In the meantime, this is a preliminary schedule.
4:00 - 4:20
welcome
4:20 - 4:45
5x lightning talks
4:45 - 5:05
break, food, demos
5:05 - 5:25
panel
5:25 - 5:50
5x lightning talks
5:50 - 6:10
break, food, demos
6:10 - 6:30
panel
6:30 - 6:55
5x lightning talks
6:55 - 7:10
closing
7:10 - 9:00
reception


In this video we learn about the Dot Com bubble era, where e-commerce startups were taking over the economy, causing investors to spend millions of dollars on various different products. Two websites that pioneered this revolution were Amazon and Ebay. For Amazon, we learn how they grew to the massive titan, they are today, also how quickly it all happened for them.
Amazon was founded in a garage by Jeff Bezos, in Seattle Washington. The purpose of amazon at first was to sell books and become one of the biggest online bookstores. It launched in the summer of 1995. Along with Amazon, Pierre Omidayar developed a website called Ebay, with the goal of online market/auction with an even field for everyone to bid away.
Amazon coined the strategy called Get Big Fast or GBF, which means pricing low and marketing heavily to build a user base. This strategy was very effective due to the r growth of the internet, gaining about 15 million users in five years.
This rapid growth of Amazon and Ebay caused people to invest heavily in their stock, not only their stock but any with a .com on it. Companies invested millions of dollars into e-commerce, web developers getting 400 million dollar checks just for the ability to build sites, in hopes of being the next amazon. Wall street and Silicon Valley became the homes of this bubble era. However, a lot of these e-commerce sites failed to replicate amazon and ebay success and on Black Friday stock markets for Nasdaq crash potentially ending the bubble era.


2017-04-26: General Discussion¶
Time: 17:00 (UTC)
Hangout link: https://hangouts.google.com/call/v5olhwzpfzgzpoq5i3wthjpqpie
Attendees
Jonathan Helmus, Eric Dill, Filipe, Peter Williams, John Kirkham, Matt Craig, Michael Sarahan
Standing Items
Notes
conda-build-all conda 4.3 - SciTools/conda build all#83
CFEP manual build and upload - conda forge/conda forge enhancement proposals#5
Agenda
Defaults channel libpng and jpeg updates: scheduled for 1Q 2017. Continuum requests closer collaboration on future api/abi incompatible updates to core libraries.
One pinning scheme for both defaults and conda-forge?
That would be ideal. Hopefully we can head that way.
We missed the opportunity to coordinate libpng, I pinned to 1.6.28 and 1-day after I found out that defaults pinned to 1.6.27.
We should coordinate to move to icu 58.* together. I already pinned in the script (which is broken BTW), but I will start sending PRs to use that ICU soon.
Drop Python 3.4. Now that conda-forge have Qt 4+jpeg9* on Windows the Python 3.5+Windows users can migrate from Python 3.4 to 3.5.
Do MinGW compile things that play well with Python 3.5/MSVC 2015 yet? AFAICT this is still an issue.
Have a VM I’m willing to use to do this. Details here.
Re-rendering channel improvements. ( conda forge/conda smithy#401 )
Build Qt 5 based on https://anaconda.org/qttesting/qt/files and manually upload it
Document when should we use
osx_is_app: True
should use this whenever a GUI is involved. This uses
pythonw
on osx instead ofpython
Status of Python 3.6 and Numpy 1.12 package: Discuss the
MNT: Re-render the feedstock [ci skip]
issue for Linux Python 3.6 packages.Take advantage of conda 4.3 new feature “Generic- and Python-Type Noarch/Universal Packages.” https://www.continuum.io/blog/developer-blog/announcing-general-availability-conda-4-3
Example of how this could be done using the imagesize package: conda forge/imagesize feedstock#3
Break linter out of
conda-smithy
into a new package (e.g.conda-lint
). conda forge/conda smithy#386Perl silliness. Too many Perls showing up after re-rendering. How to fix? conda forge/conda smithy#415
Use Travis-CI auto-cancel feature
Discuss how to enable R builds. We do not have win32 builds of r-base and no way to deal with the CONDA_R=
yet. When building R packages, there is often a timeout when downloading the sources from CRAN. Maybe the connect and read timeout in conda-build can be made configurable?
Manual upload of VTK due to CI limitation.
Discuss R migration effort
Discuss staged-recipes/Travis CI failures.
Discuss revamping team update as a webservice ( conda forge/conda forge webservices#63 )
Note the current team update has been failing for months and only completes a little more than half the teams.
Adding other specialized teams.
consider switching to dropbox paper (as the bar at the top of hackpad suggests). I know that jupyter has switched to dropbox paper for their dev meetings. (Apparently this is no longer optional) (admin of the conda-forge group on hackpad can “migrate” these hackpads to paper: )https://www.dropbox.com/help/9156#import)
Guidance for renaming conda packages and its impact on the feedstock. tl;dr do you (a) submit a new recipe to staged recipes and deprecate the old one or (b) update the feedstock in-place
Discuss updated pinning handling ( conda forge/staged recipes#2267 ) ( conda forge/conda smithy#482 ).
Suggestion: Match name the pinning file will have in conda build 3.
Dropping Obvious-CI from the docker image ( conda forge/docker images#47 ).
X11 CFEP
Omnia moving to conda-forge


Guillaume Calmettes
Importance of visualizing your data
Know/Describe your dataset
How to present categorical and quantitative variables
Shape
Central tendency
Spread
A key step in the statistical investigation method is drawing conclusions beyond the observed data. Statisticians often call this statistical inference.
They are 4 main types of conclusions (inferences) that
we can draw from data:
Could the observed result be considered statistically significant?
Is our result unlikely to happen by random chance?
Dr. Jarvins Bastian's experiment (1964)
In one phase of the study, Dr. Bastian had Buzz attempt to push the correct button
a total of 16 different times.
=> Buzz pushed the correct button
15 out of 16 times. ($\overset{\hat{}}{p} = \frac{15}{16} = 0.938$)
Think about it:
Based on these data, do you think Buzz somehow knew which button to push?
Is 15 out of 16 correct pushes convincing to you?
Or do you think that Buzz could have just been guessing?
Buzz is just guessing (his probability of choosing the correct button is 0.50) and he got really lucky in these 16 attempts. $\pi=0.5$
Buzz is doing something other than just guessing (his probability of choosing the correct button is more than 0.50). $\pi>0.5$
The key question here is to determine what results would occur in the long-run under the assumption that Buzz is just guessing.
=>
We call this assumption of random guess by Buzz the null hypothesis (or null model)
How could we decide between our 2 hypotheses?
Statisticians often employ chance models to generate data from random processes to help them investigate such processes.
What probability do we need to simulate to test our hypothesis?
=> 50/50 chance model
What would be a good simulation model for Buzz & Doris communication experiment?
=> coin flip
| Model | Experiment |
|---|---|
| Coin flip | Button choice by Buzz |
| Heads | Correct button |
| Tails | Wrong button |
| Chance of heads | Probability of Buzz pressing the correct button |
| One set of 16 coin flips |
One set of 16 experiments |
If Buzz was just guessing which button to push each time, what would be the number of correct choices we would observe for 16 attempts?
https://goo.gl/cYUbFn
1- Flip a coin 16 times
2- Record the number of heads that you obtain
3- Enter this value in the spreedsheet in the cell assigned to your name (please only fill up the value for your name!)
What are the:
observational units?
variables?
Hint: what does each data point represent?
Each dot represents the
number of heads
in
one set of 16 coin tosses
Does it seem that the number of correct button choices by Buzz would have been surprising if in fact he was just guessing?
We really need to simulate this random selection process hundreds, preferably thousands of times to obtain the long-run pattern of our simulation.
This would be very tedious and time-consuming with coins to obtain the long-run distribution of the number of heads for 16 coin-flips, so we'll turn to technology to simulate it.
How many of these 1000 simulations produced 15 or more correct choices by Buzz?
1 out of 1000
What is the corresponding proportion of simulations that produced such an extreme result?
$\frac{1}{1000}=0.001$
A p-value is the probability of obtaining a result as extreme as the one observed, assuming that the null hypothesis is true.
A small p-value casts doubt on the null hypothesis/model used to perform the calculation (in this case, that Buzz was just guessing which button to push).
A p-value
$\leq{0.10}$
$\leq{0.05}$
$\leq{0.01}$
$\leq{0.001}$
is generally considered
to be
some
fairly strong
very strong
extremely strong
evidence against the null
The results ($\textrm{p-value}=\frac{1}{1000}=0.001$) mean our evidence is strong enough to be considered statistically significant. That is, we don't think our study result (15 out of 16 correct) happened by chance alone, but rather, something other than "random chance" was at play.
One goal of statistical significance testing is to rule out random chance as plausible (believable) explanation for what we have observed. We still need to worry about how well the study was conducted.
- Could Buzz see the light through the curtain?
- Could Buzz have detected a pattern in the succession of light signal?
- We haven't completely rule out random chance (but probability is very small)
One option that Dr. Bastian pursued was to redo the study except now he replaced the curtain with a wooden barrier between the two sides of the tank in order to ensure more complete separation between Doris and Buzz.
=> In this case, Buzz pushed the correct button only 16 out of 28 times.
Buzz' successes: 16 out of 28 ($\overset{\hat{}}{p} = \frac{16}{28} = 0.57$)
Simulations:
This time we need to do repetitions of
28 coin flips, not just 16.
Buzz' successes: 16 out of 28 ($\overset{\hat{}}{p} = \frac{16}{28} = 0.57$)
Simulations:
This time we need to do repetitions of
28 coin flips, not just 16.
p-value:
$\frac{2795}{10000}=0.280$
Not enough evidence that the "by-chance-alone" model
is wrong.
In fact, Dr. Bastian soon discovered that in this set of attempts the equipment malfunctioned and the food dispenser for Doris did not operate and so Doris was not receiving her fish rewards during the study.
=>
It is not so surprising that removing the incentive hindered the communication between the dolphins and we cannot refute that Buzz was not guessing.
Dr. Bastian fixed the equipment and ran the study again. This time he found convincing evidence that Buzz was not guessing.
Conclusion/Generalization:
Dolphins can communicate abstract concepts!
1- Collect your sample and calculate your statistic of interest (ex: $\overset{\hat{}}{p}=\frac{17}{24}=0.708$)
2- State your null and alternative hypothesis
(ex: H$_0$ $\pi=0.5$ / H$_a$ $\pi>0.5$)
3- Simulate your null hypothesis distribution. (it should be centered at the stated H$_0$ parameter of interest)
4- Calculate the proportion of samples that resulted in cases at least as extreme
as your initial observed statistic. This is your p-value.
(ex: $\textrm{p-value}=\frac{289}{10000}=0.029$)
5- Conclude about whether your have evidence in favor or against the null hypothesis.
Another way (than p-value) to measure strength of evidence is to
standardize the observed statistic by
measuring how far it is from the mean of the
null distribution using standard deviation units.
This measure is commonly noted $z$.
$z=\frac{\textrm{observed statistic-}\fragindex{2}{\fraglight{highlight-blue-gc}{\textrm{mean of null distribution}}}}{\fragindex{4}{\fraglight{highlight-red-gc}{\textrm{standard deviation of null distribution}}}}$
Mean of null distribution:
$\mu = 0.5$ ($\pi$)
SD of null distribution:
???
In the early 1900s (and even earlier), computers weren't available to do simulations, and as peole didn't want to sit around and flip coins all day long, they focused their attention on mathematical and probabilistic rules and theories that could predict what would happen if someone did simulate.
They proved the following result:
Central limit theorem:
If the sample size (n) is large enough, the distribution of sample
proportion will be bell-shaped (or normal), centered at the long-run
proportion, with a standard deviation of
$\sqrt{\frac{\pi(1-\pi)}{n}}$
Validity conditions:
The normal approximation can be thought of as a prediction of what would occur if
simulation was done. Many times this prediction is valid, but
not always. The prediction is considered valid when there
are at least 10 successes and 10 failures in the sample.
Applied to the coin flip model, this correspond to:
In the second experiment, Buzz got 16 correct choices out of 28 attempts.
Buzz got 16 correct choices out of 28 attempts
$z=0.756$the number of correct choices is 0.756 SD away from the mean of the null normal distribution
Note:
Mathematically, it is possible to calculate the exact probability of getting $\geq16$ heads in 28 tosses using the binomial distribution.
p = 0.2858
Note:
Mathematically, it is possible to calculate the exact probability of getting $\geq16$ heads in 28 tosses using the binomial distribution.
p = 0.2858
Which method gives the p-value closest to the true probability? Why?
Lots of assumptions & required validity conditions!
| Resampling simulation | Normal approximation (Z-test) |
|---|---|
| Collect your sample and calculate your statistic of interest ($\overset{\hat{}}{p}$) | |
| State your null (H$_0$) and alternative (H$_a$) hypotheses | |
| Directly simulate your null hypothesis distribution (this results in a resampling distribution centered at the stated H$_0$ proportion $\pi$) |
Consider the normal distribution centered at the long-run
H$_0$ proportion $\pi$, with a standard deviation of
$\sqrt{\frac{\pi(1-\pi)}{n}}$
|
|
Calculate the proportion of resampling samples that resulted in cases at least as extreme
as your initial observed statistic. This is your p-value |
Determine the proportion of area under the curve that is at least as extreme
as your initial observed statistic. This is your p-value |
| Conclude about whether your have evidence in favor or against the null hypothesis |


About
Work
Contact
Hello! My name is Steve Golden.
I am excited about technolgies and I am...
a web developer
Here are some of my projects
Tribute Page
Random Quote Machine
Javascript Calculator
Map Data across the Globe
Wikipedia Viewer
Tic Tac Toe Game
Show all
Let's work together...
How do you take your coffee?
Facebook
Github
Twitter
Send an email
Call me


Intention:
Live with intention, Walk to the edge.
Continue to Learn. Play with abandon, Choose with no regret. Laugh! Do what you love. Live as if this is all there is.
developing sites :: branding
Aesthetic Prosthetics Logo Exploration
Aesthetic Prosthetics Site (LIVE)
New Smile Dental


The message toast is a small, non-disruptive popup for success messages that disappears automatically after a few seconds. When message toast is displayed, it is always centered horizontally at the bottom of the screen and fades in and out automatically.
Note: By default, Message Toast uses default MessageToastConfig
configuration.
Users can override this configuration with MessageToastModule.withConfig(config)
method when
importing MessageToastModule
module.
Default timing and duration of the message toast is 3000 ms. The duration can be above 3000 ms, but it's not
recommended to be less than 3000 ms.


HTML and CSS with a mobile first approach
ToDo App Project
CSS lab: New Assembly
CSS lab:The Jungle
© 2019 Tajin Joya.


THE Mouse went to visit the Cat, and found her sitting behind the hall door, spinning.
MOUSE
What are you doing, my lady, my lady,
What are you doing, my lady?
CAT (sharply)
I'm spinning old breeches, good body, good body,
I'm spinning old breeches, good body.
MOUSE
Long may you wear them, my lady, my lady,
Long may you wear them, my lady.
CAT (gruffly)
I'll wear 'em and tear 'em, good body, good body,
I'll wear 'em and tear 'em, good body.
MOUSE
I was sweeping my room, my lady, my lady,
I was sweeping my room, my lady.
CAT
The cleaner you'd be, good body, good body,
The cleaner you'd be, good body.
MOUSE
I found a silver sixpence, my lady, my lady,
I found a silver sixpence, my lady.
CAT
The richer you were, good body, good body,
The richer you were, good body.
MOUSE
I went to the market, my lady, my lady,
I went to the market, my lady.
CAT
The further you went, good body, good body,
The further you went, good body.
MOUSE
I bought me a pudding, my lady, my lady,
I bought me a pudding, my lady.
CAT (snarling)
The more meat you had, good body, good body,
The more meat you had, good body.
MOUSE
I put it in the window to cool, my lady,
I put it in the window to cool.
CAT (sharply)
The faster you'd eat it, good body, good body,
The faster you'd eat it, good body.
MOUSE (timidly)
The cat came and ate it, my lady, my lady,
The cat came and ate it, my lady.

(Springs upon the mouse and kills it.)


derivatives.Rd
Derivatives of estimated smooths via finite differences
derivatives(object, ...) # S3 method for default derivatives(object, ...) # S3 method for gamm derivatives(object, ...) # S3 method for gam derivatives(object, term, newdata, order = 1L, type = c("forward", "backward", "central"), n = 200, eps = 1e-07, interval = c("confidence", "simultaneous"), n_sim = 10000, level = 0.95, unconditional = FALSE, frequentist = FALSE, offset = NULL, ...)
| object | an R object to compute derivatives for. |
|---|---|
| ... | arguments passed to other methods. |
| term | character; vector of one or more smooth terms for which derivatives are required. If missing, derivatives for all smooth terms will be returned. Can be a partial match to a smooth term. |
| newdata | a data frame containing the values of the model covariates at which to evaluate the first derivatives of the smooths. |
| order | numeric; the order of derivative. |
| type | character; the type of finite difference used. One of
|
| n | numeric; the number of points to evaluate the derivative at. |
| eps | numeric; the finite difference. |
| interval | character; the type of interval to compute. One of
|
| n_sim | integer; the number of simulations used in computing the simultaneous intervals. |
| level | numeric; |
| unconditional | logical; use smoothness selection-corrected Bayesian covariance matrix? |
| frequentist | logical; use the frequentist covariance matrix? |
| offset | numeric; a value to use for any offset term |
library("mgcv")dat <- gamSim(1, n = 400, dist = "normal", scale = 2, verbose = FALSE) mod <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = "REML") ## first derivative of all smooths derivatives(mod)#> # A tibble: 800 x 8 #> smooth var data derivative se crit lower upper #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 s(x0) x0 0.000239 7.41 3.33 1.96 0.874 13.9 #> 2 s(x0) x0 0.00525 7.41 3.33 1.96 0.875 13.9 #> 3 s(x0) x0 0.0103 7.40 3.33 1.96 0.884 13.9 #> 4 s(x0) x0 0.0153 7.40 3.31 1.96 0.902 13.9 #> 5 s(x0) x0 0.0203 7.39 3.30 1.96 0.929 13.8 #> 6 s(x0) x0 0.0253 7.38 3.27 1.96 0.965 13.8 #> 7 s(x0) x0 0.0303 7.36 3.24 1.96 1.01 13.7 #> 8 s(x0) x0 0.0353 7.34 3.20 1.96 1.07 13.6 #> 9 s(x0) x0 0.0403 7.32 3.15 1.96 1.14 13.5 #> 10 s(x0) x0 0.0453 7.29 3.10 1.96 1.21 13.4 #> # ... with 790 more rows## second derivative of smooth of x2 only using central finite difference derivatives(mod, "s(x2)", order = 2, type = "central")#> # A tibble: 200 x 8 #> smooth var data derivative se crit lower upper #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 s(x2) x2 0.00359 -9.06 12.0 1.96 -32.5 14.4 #> 2 s(x2) x2 0.00860 20.1 12.8 1.96 -5.05 45.2 #> 3 s(x2) x2 0.0136 -1.64 6.94 1.96 -15.2 12.0 #> 4 s(x2) x2 0.0186 19.5 18.6 1.96 -17.0 55.9 #> 5 s(x2) x2 0.0236 21.7 29.9 1.96 -36.9 80.2 #> 6 s(x2) x2 0.0286 34.8 36.2 1.96 -36.1 106. #> 7 s(x2) x2 0.0336 66.7 54.7 1.96 -40.4 174. #> 8 s(x2) x2 0.0386 66.1 66.1 1.96 -63.4 196. #> 9 s(x2) x2 0.0436 90.1 80.3 1.96 -67.3 247. #> 10 s(x2) x2 0.0486 83.2 86.5 1.96 -86.4 253. #> # ... with 190 more rows


A dotpath is a notation to reference elements of a JSON object. It includes the ability to traverse nested arrays. The root of a is a leading dot. A dot is typed as a period (i.e. "."). Given the following the dot path to the "name" field would be ".name".
{"name":"Jane Doe"}
The dotpath .name
would return the value "Jane
Doe".
Arrays are designated with square brackets (e.g. [0] would reference the first element of an array, arrays are number from zero).
["one", "two", "three"]
The dotpath of [0]
would correspond to the value "one",
[1]
would correspond to the value "two" and
[2]
would refer to the value "three". If you wish to
include all the elements of an array you would use [:]
.
This would return the full array. Likewise if you need the second until
end of the array you would get the values with [2:]
.
Finally if you only wanted the first and second element you could refer
to it with the dotpath [0:1]
.
Often you have more complex objects including some level of nesting. Element(s) can be reference by combine the dotpaths into more complex expressions.
{
"title": "Introducing dataset",
"authors":[
{"given_name":"Tom", "family_name":"Morrell"},
{"given_name":"Robert","family_name": "Doiel"}
]
}
You would reference the title with .title
, the first
author's family name with .authors[0].family_name
or get an
array of authors family names with
.authors[:].fmaily_name
.


For browser usage only:
Create your project using this package.json. After downloaded, run npm install and start writing your application.
npm install
In the browser:


Here are some things that I've created!
{%
include portfoliocard.html
title="Example Project 1"
description="This is an example portfolio card with an image. Normally a description of the project would go here."
%}
{%
include portfoliocard.html
title="Example Project 2"
description="This is an example portfolio card with an image. Normally a description of the project would go here."
%}
{%
include portfolioitem.html
title="Example Project 3"
description="This is an example portfolio item. Normally a description of the project would go here."
%}
{%
include portfolioitem.html
title="Example Project 4"
description="This is an example portfolio item. Normally a description of the project would go here."
%}


Pending Count: {{pendingCount}}
In Progress Count: {{inProgressCount}}
Job In Progress
No Worker Available
Cancelled: {{cancelled}}
Job Completed
{{work.errorLog}}
{{work.stdLog}}


A late night after movie chocolate craving brought me right in front of Starbucks, one of the only cafe’s open at midnight. After going through the selection of desserts, I decided to devour a Molten Chocolate Cake (who can say no?)
Now before you go all how-can-you-commit-a-sin-against-the-holy-grail-of-molten-chocolate-cakes(Chillis to the non enlightened crowd) I merely wanted to try it. Besides, I didnt have much choice a that time of the night. So how was it? Bad.
Dont get me wrong here. Molten Chocolate cake in my opinion, has to be made in a particular manner- Rich chocolate cake with a soft, liquid chocolate center. Oh, and served hot.
Naturally when a friendly(sarcastic) waiter asked if I would like my cake heated up, I was taken aback.. But of course it needs to be served hot! Cardinal rule in molten goodness isnt it? So it was popped into a microwave and was merrily swirling around while I made myself comfortable, ready to dive into the warmth of molten chocolate heaven, with rich, moist cake and oozing molten center (yes I was dreaming Chillis here)
What I ended up getting was lukewarm cake — I admit it was rich and moist and not crumbly — with a HARD center. You read that right, it was not a liquid center, rather like having a bar of chocolate stuck into your cake. I finished the cake bit and left the center. Sigh.
Apart from bursting my dream sequence bubble, Im sure I could hear a snigger or two from the Chillis.
Lesson: Never mess with Chillis.
PS: I was so disappointed, I only took one photo.


Confirm Terminate Instances
You have selected [[selectedTags ]] instances . Please confirm your selection.
Terminated
instances are not recoverable.


There is one consistent factor no matter how young or old you are, your successes and struggles, where you are from, or what you are facing right now, motivational quotes will spur you on, giving you the much needed impetus to face whatever life throws at you. Quotes are priceless sources of wisdom and guidance when most needed. During times of trials, struggles and tribulations, they definitely motivate. Failure and success are never final in life; we can always do better, improve or even turn situations around. Inspirational quotes are not only timeless but are often timely in many situations. This is indeed the reason why motivational quotes are important for everyone!


| DOSE: |
1 tablet |
|
MDMA |
(pill / tablet) |
| |
1 tablet |
insufflated |
MDMA |
(pill / tablet) |
| |
1 tablet |
|
MDMA |
(pill / tablet) |
Iíve taken E quite a few times. The basis for this was solely on recreational use. My last experience, on October 1st, 2006, is why I am submitting this message.
To be frank, my first experience with E was the most enlightening. I wasnít sure of its purity, for I was only 16 years old. However, I did know the dealer, and I knew that he had taken it before, so I wasnít concerned. Having taken it with my best friend, he and I found ourselves sitting at our local city beach for countless hours talking basically about life. Everything that happened that day I canít recall, but I do remember our conversation. It was profound, inspirational, and uplifting. I can still sense the connection that he and I had during our roll.
That experience was the only experience that I felt had some purpose. By purpose, I mean that he and I shared things that I wouldnít have normally shared, even to this day. I began to use E on a regular basis, 2-3 times a week. The first time had persuaded me to do it again, for I knew how unremarkable it was. It didnít have to be with my friend, or any friend for that matter. I just took it.
October 1st, 2006:
Iím pretty well educated in terms of psychoactive drugs and their effects.  Iíve had heavier doses, but not to the extent of 3 white ladies. I hadn't taken anything else that day, so I felt pretty clear-minded.
It was mid-afternoon, and the first of three kicked in roughly around 3 p.m. Feeling the effects, but wanted to kick it up a notch, so I snorted another, and for about two hours thereafter, I was in oblivion. I could sense a pretty intense come-down, which I inevitably tried to avoid, so I took one more. This took place in my dorm room while my roommate was studying. As I reached the top of my roll, my mind undoubtedly started to race. This never happened before. I thought that I had a concise perspective on what was going on, but I didnít. I decided to get some fresh air.
While outside, my friend from my hometown calls. He explains to me that one of our buddies from the neighborhood died that day. He overdosed on methadones, which is an abundant drug where I come from. Being that I was buzzed out of my mind, the realization of it didnít seep in. My mind was still racing. Going from a very euphoric sensation, to a desolate depressive state. Then, the realization sunk in. My friend died. I went back inside the dormitory and sat in the lobby. It was there where I realized that this isnít my life. I broke down, crashed, in front of what seemed to be 50 residents. A number of people came up and asked what was wrong, but I couldnít speak. My lips felt glued together, and my jaws restless as ever. I sat there thinking, not so much thinking about my friend who passed, but about my own life, my own mortality. It was this very crash that I realized drugs were no longer a part of my life.
To those who are curious about E, or any psychoactive, please understand before you take them that they are in our lives for a reason. The first time I will never forget, nor will I forget my last. The problem is that I canít remember any other time I took it. Understand that experimenting with drugs for a legitimate reason is one thing, but realize that surpassing the phase of experimentation is inevitable. Realize that your life is more than taking drugs.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Hey, seems like you are using this Application for the first time, meaning you now have the chance to set some options. If the application automatically set a save folder please also check it to be sure that it is correct.
Folder Path
Open
Check for Updates on application start
Disabled
Enabled
Input a port (0 chooses a random available port) - only applied after application restart
Enable "Expert" mode
Disabled
Enabled
This disables all the checks the application does for invalid data, meaning the likelihood of you breaking your save with a non existent item f.e. is increased. But if you know the internal id of an item and it doesn't exist, you can add it with this option. This option does not require an application restart, but it requires a page reload (so that the settings are loaded again).
Stranger Sins Support
Disabled
Enabled
Game Of Crone Support
Disabled
Enabled
Better Save Soul Support
Disabled
Enabled
This allows you to select which DLC you own.
Depending on owned DLC different autocomplete names are shown. Additionally some buttons have different functionality depending on the DLCs.
Up to how many backups should be kept by the editor when saving? (Each save)
Backup saves are automatically zipped with deflate to save space on the disk. It is intended and correct that the name within the zip is always the same ([...].back_1).
Done


This property specifies the width of the four borders:
Hello..!!.
Holla....!!.
Good Morning...!!
Good Evening...!!
Good Night....!!
Welcome...!!
The border-width property can have from one to four values (for the top border, right border, bottom border, and the left border):
Some text.
Some text.
Some text.
This property specifies the color of the four borders:
A solid red border
A solid green border
A dotted blue border
Note: The "border-color" property does not work if it is used alone. Use the "border-style" property to set the borders first.


derelict.util.sharedlib
-
Declaration
struct
SharedLib
;Low-level wrapper of the even lower-level operating-specific shared library loading interface.
Discussion
While this interface can be used directly in applications, it is recommended to use the interface specified by derelict.util.loader.SharedLibLoader to implement bindings.
SharedLib
is designed to be the base of a higher-level loader, but can be used in a program if only a handful of functions need to be loaded from a given shared library.-
Declaration
void
load
(string[]names
);Finds and loads a shared library, using
names
to find the library on the file system.Discussion
If multiple library
names
are specified innames
, a SharedLibLoadException will only be thrown if all of the libraries fail toload
. It will be the head of an exceptin chain containing one instance of the exception for each library that failed.
Parameters
string[]
names
An array containing one or more shared library
names
, with one name per index.Throws
SharedLibLoadException if the shared library or one of its dependencies cannot be found on the file system. SymbolLoadException if an expected symbol is missing from the library.
-
Declaration
void*
loadSymbol
(stringsymbolName
, booldoThrow
= true);Loads the symbol specified by
symbolName
from a shared library.Parameters
string
symbolName
The name of the symbol to load.
bool
doThrow
If
true
, a SymbolLoadException will be thrown if the symbol is missing. Iffalse
, no exception will be thrown and the ptr parameter will be set tonull
.Throws
SymbolLoadException if
doThrow
istrue
and a the symbol specified by funcName is missing from the shared library. -
Declaration
void
unload
();Unloads the shared library from memory, invalidating all function pointers which were assigned a symbol by one of the load methods.
-
Declaration
@property string
name
();Returns the
name
of the shared library. -
Declaration
@property bool
isLoaded
();Returns
true
if the shared library is currently loaded,false
otherwise. -
Declaration
@property void
missingSymbolCallback
(MissingSymbolCallbackDgcallback
);Sets the
callback
that will be called when an expected symbol is missing from the shared library.Parameters
MissingSymbolCallbackDg
callback
A delegate that returns a value of type derelict.util.exception.ShouldThrow and accepts a string as the sole parameter.
-
Declaration
@property void
missingSymbolCallback
(MissingSymbolCallbackFunccallback
);Sets the
callback
that will be called when an expected symbol is missing from the shared library.Parameters
MissingSymbolCallbackFunc
callback
A pointer to a function that returns a value of type derelict.util.exception.ShouldThrow and accepts a string as the sole parameter.
-
Declaration
@property MissingSymbolCallback
missingSymbolCallback
();Returns the currently active missing symbol callback.
Discussion
This exists primarily as a means to save the current callback before setting a new one. It's useful, for example, if the new callback needs to delegate to the old one.
-