Cropping figures in latex

Image you have a figure, but it just needs a bit of tweaking. Fear not! the following will do all the magic for you:

\usepackage{graphicx}
...
\begin{figure}[htbp]
    \begin{center}
        \includegraphics[trim=left bottom right top, clip]{file}
    \caption{default}
    \label{default}
    \end{center}
\end{figure}

, where the default units of trim are big points (bp). For example:
trim=1200 400 1500 1500, clip]

I recommend you start small (say 100) and then go up from there, depending on the need.
Taken from here.

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Partial list of Conditionally Accepted SIGGRAPH ASIA 2018 papers

Apologies for typos and the lack of a clear formatting, I copied pasted from other web sites without editing:

  • C. Li, H. Pan, Y. Liu, X. Tong, A. Sheffer, W. Wang,  Robust Flow-Guided Neural Prediction for Sketch-Based Freeform Surface Modeling , ACM Transactions on Graphics (Proc SIGGRAPH Asia) 2018, accepted. link
  • M Li, D. M. Kaufman, V. G. Kim, J. Solomon, A. ShefferOptCuts: Joint Optimization of Surface Cuts and Parameterization , ACM Transactions on Graphics (Proc SIGGRAPH Asia) 2018, accepted. link
  • Lavenant, Hugo, Sebastian Claici, Edward Chien, and Justin Solomon. “Dynamical Optimal Transport on Discrete Surfaces.” SIGGRAPH Asia 2018, Tokyo (to appear). link
  • GPU Optimization of Material Point Methods , Ming Gao*, Xinlei Wang*, Kui Wu* (equal contributions), Andre Pradhana-Tampubolon, Eftychios Sifakis, Cem Yuksel, Chenfanfu Jiang. link
  • Narrow-Band Topology Optimization on a Sparsely Populated Grid 
    Haixiang Liu*, Yuanming Hu* (joint first authors), Bo Zhu, Wojciech Matusik, Eftychios Sifakis. link
  • Deep Multispectral Painting Reproduction via Multi-layer, Custom-Ink Printing
    Liang Shi, Vahid Babaei, Changil Kim, Michael Foshey, Yuanming Hu, Pitchaya Sitthi-Amorn, Szymon Rusinkiewicz, Wojciech Matusik link
  • Guowei Yan, Wei Li, Ruigang Yang and Huamin Wang. 2018. Inexact Descent Methods for Elastic Parameter Optimization. ACM Transactions on Graphics (SIGGRAPH Asia), conditionally accepted. link
  • Multi-chart Generative Surface Modeling
    Heli Ben-Hamu, Haggai Maron, Itay Kezurer, Gal Avineri, Yaron Lipman
    Conditionally accepted to ACM SIGGRAPH Asia 2018. link
  • Chenyang Zhu, Kai Xu*, Siddhartha Chaudhuri, Renjiao Yi and Hao Zhang, “SCORES: Shape Composition with Recursive Substructure Priors, ACM Transactions on Graphics (SIGGRAPH Asia 2018), 37(6). link
  • Xiaogang Wang, Bin Zhou, Haiyue Fang, Xiaowu Chen, Qinping Zhao and Kai Xu*, “Learning to Group and Label Fine-Grained Shape Components, ACM Transactions on Graphics (SIGGRAPH Asia 2018), 37(6). Conditionally accepted. link
  • Hao Wang, Nadav Schor, Ruizhen Hu, Haibin Huang, Daniel Cohen-Or, Hui Huang
    Conditionally Accepted to ACM SIGGRAPH ASIA 2018
  • Li Yi, Haibin Huang, Difan Liu, Evangelos Kalogerakis, Hao Su, Leonidas Guibas
    Conditionally Accepted to ACM SIGGRAPH ASIA 2018
  • 3D Hair Synthesis Using Volumetric Variational Autoencoders
    Shunsuke Saito, Liwen Hu, Chongyang Ma, Hikaru Ibayashi, Linjie Luo, Hao Li link
  • N. Smith, N. Moehrle, M. Goesele, W. Heidrich:
    Aerial Path Planning for Urban Scene Reconstruction — A Continuous Optimization Method and Benchmark
    ACM Transactions on Graphics (Proc. SIGGRAPH Asia), 2018 link
  • Efficient palette-based decomposition and recoloring of images via RGBXY-space geometry
    Jianchao Tan, Jose Echevarria, Yotam Gingold
    ACM Transactions on Graphics (TOG) 37(6). To be presented at SIGGRAPH Asia 2018.  link
  • Modeling Hair from an RGB-D Camera
    Meng Zhang, Pan Wu, Hongzhi Wu, Yanlin Weng, Youyi Zheng, Kun Zhou
    ACM Transactions on Graphics (SIGGRAPH ASIA 2018) link

  • Warp-guided GANs for Single-Photo Facial Animation
    ACM Transactions on Graphics (SIGGRAPH ASIA 2018)
    Jiahao Geng, Tianjia Shao, Youyi Zheng, Yanlin Weng, Kun Zhou link

  • paGAN: Real-time Avatars Using Dynamic Textures
    Koki Nagano, Jaewoo Seo, Jun Xing, Lingyu Wei, Zimo Li, Shunsuke Saito, Aviral Agarwal, Jens Fursund, Hao Li
    ACM Transactions on Graphics (SIGGRAPH ASIA 2018)
    [Live Demo] [RTL] [FXGUIDE] link
  • CreativeAI: Deep Learning for Computer GraphicsNiloy J. Mitra, Iasonas Kokkinos, Paul Guerrero, Nils Thuerey, Tobias Ritschel conditionally accepted to SIGGRAPH Asia 2018 link
  • FrankenGAN: Guided Detail Synthesis for Building Mass-Models Using Style-Synchonized GANs
    Tom Kelly*, Paul Guerrero*, Anthony Steed, Peter Wonka^, Niloy J. Mitra^
    SIGGRAPH Asia 2018 link
  • Learning a Shared Shape Space for Multimodal Garment Design
    Tuanfeng Wang, Duygu Ceylan, Jovan Popovic, Niloy J. Mitra
    SIGGRAPH Asia 2018 link
  • Aerobatics Control of Flying Creatures via Self-Regulated Learning, SIGGRAPH Asia 2018.  Jehee Lee and colleagues. link
  • Interactive Character Animation by Learning Multi-Objective Control, SIGGRAPH Asia 2018. Jehee Lee and colleagues. link
  • Learning to Dress: Synthesizing Human Dressing Motion via Deep Reinforcement Learning, Alex Clegg, Wenhao Yu, Jie Tan, C. Karen Liu and Greg Turk, in Transactions on Graphics (SIGGRAPH Asia), 2018 [PDF]  [Video] link

  • “Hybrid Grains: Adaptive Coupling of Discrete and Continuum Simulations of Granular Media,” Yonghao Yue*, Breannan Smith*, Peter Yichen Chen*, Maytee Chantharayukhonthorn*, Ken Kamrin+, and Eitan Grinspun+ (*: co-first authors, +: corresponding authors). 2018. ACM Transactions on Graphics, Vol.37, No.6 (Proc. SIGGRAPH ASIA 2018). Tokyo, Japan. [PDF] [Video] [Bibtex] [Project] link
  • Hsueh-Ti Derek Liu, Michael Tao, Alec Jacobson. “Paparazzi: Surface Editing by way of Multi-View Image Processing” ACM Transactions on Graphics (Proc. SIGGRAPH Asia), 2018. link
  • “Geometry-Aware Metropolis Light Transport”
    H. Otsu, J. Hanika, T. Hachisuka, and C. Dachsbacher
    ACM Transactions on Graphics (SIGGRAPH Asia 2018), 2018 link
  • Mingming He, Dongdong Chen, Jing Liao, Pedro V. Sander, Lu Yuan.
    Deep Examplar-Based Colorization.
    ACM Transactions on Graphics (SIGGRAPH 2018) [conditionally accepted]. link
  • Multi-view Wire Art
    Kai-Wen Hsiao, Jia-Bin Huang, and Hung-Kuo Chu
    (Conditionally accepted)
    ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia), 2018
    [Paper (PDF)] [Project page] [Code] link
  • Decoupling Simulation Accuracy from Mesh Quality
    Teseo Schneider, Yixin Hu, Jérémie Dumas, Xifeng Gao, Daniele Panozzo, Denis Zorin.
    ACM Transactions on Graphics (SIGGRAPH Asia 2018)
    [paper], [code(todo)]. link
  • SIGGRAPH Asia 2018

    pdf | webpage link

  • Stabilized Real-time Face Tracking via a Learned Dynamic Rigidity Prior
    ACM Transactions on Graphics (Proc. SIGGRAPH Asia), December 2018
    Chen Cao, Menglei Chai, Oliver Woodford and Linjie Luo.  link
  • George E. BrownMatthew OverbyZahra Forootaninia, and Rahul Narain.
    “Accurate Dissipative Forces in Optimization Integrators”.
    ACM Transactions on Graphics (Proc. SIGGRAPH Asia), 2018, to appear. link
  • Crowd Space: A Predictive Crowd Analysis Technique,
    Ioannis Karamouzas, Nick Sohre, Ran Hu, and Ioannis Karamouzas
    In SIGGRAPH Asia 2018 (to appear).
    [pdf] [bib] [project] link

Predatory journals

From Wikipedia: “Predatory open-access publishing is an exploitative open-access academic publishing business model that involves charging publication fees to authors without providing the editorial and publishing services associated with legitimate journals (open access or not). The idea that they are “predatory” is based on the view that academics are tricked into publishing with them, though some authors may be aware that the journal is poor quality or even fraudulent.”

Personally, I get a lot of spam email from these type of journals. This site keeps a list of such journals. That being said, because of such spam, I am not sure whether journals that I am unfamiliar with or are new are predatory or not:

Dear …,

We came across a very interesting article of yours:

….

On behalf of our Guest Editors Prof. Andrew Ware (University of South
Wales, UK) and Prof. Athanasios G. Malamos (Technological Educational
Institution of Crete, Greece), we would like to invite you to contribute
either a review or a research paper, to be published in the Special
Issue “Artificial Intelligence Supported Design and Innovation” of the
journal Designs (ISSN 2411-9660; http://www.mdpi.com/journal/designs).

*Special Issue Scope*
The primary objective of the Special Issue is to present a coherent and
comprehensive analysis of developments relating to the application of
artificial intelligent techniques in the field of design and innovation.
The topics include, but are not limited to the following: Creativity;
Intelligent design; Computer-aided design; Algorithm-driven design; UI
and UX design; Games design; Planning design; Personal Assistant systems
design; Optimization/evaluation in engineering design; Sensing and AI
signal processing and control; Cognitive design; Decision support; Deep
learning; Crowdsourcing design […]
More details about this Special Issue can be found at:
http://www.mdpi.com/journal/designs/special_issues/artificial_intelligence_Design

*Author Benefits*
Designs is one of MDPI’s open access journals, which covers all aspects
of *Engineering Designs* research. Our authors enjoy the benefits of:
1. *No charges* for well-prepared contributions; free English editing
service after acceptance;
2. Open access and high visibility: unlimited access, various
promotional activities and academic event presentations;
3. Rapid response from submission to first decision within approx. 3
weeks, publication within approx. 6 weeks;
4. No restrictions on the length of manuscripts.

The official deadline for full paper delivering is 20 *February* 2019.
You may submit your manuscript at any time until this deadline. Accepted
papers will be published immediately, and do not need to wait other
planned manuscripts.

Should you have any questions, please feel free to contact us. We look
forward to having a cooperation with you on this project. 🙂

Best regards,

Mr. Ryan Pei
Managing Editor, Designs

On behalf of Guest Editors

Prof. Dr. Andrew Ware
Faculty of Computing, Engineering and Science,
University of South Wales,
Pontypridd, CF37 1DL, UK

Prof. Dr. Athanasios G. Malamos
Multimedia, Networks and Communications Laboratory,
Department of Informatics Engineering,
Technological Educational Institution of Crete,
Estavromenos 71401, Heraklion Crete, Greece

MDPI, Designs Editorial Office
St. Alban-Anlage 66, 4052 Basel, Switzerland
Email: designs@mdpi.com
http://www.mdpi.com/journal/designs

 

 

Finding Duplicate Latex Labels

While working on a my dissertation, I noticed that LaTeX was giving me a warning message:
LaTeX Warning: There were multiply-defined labels.

Since LaTeX didn’t feel the need to tell me which labels were multiply-defined, I had to find them myself. Finding these by hand is hard to impossible. Luckily, this perl script solves the problem:

perl -nE 'say $1 if /(\\label[^}]*})/' *.tex | sort | uniq -c | sort -n

Taken from here.

(Mac OS X) Producing iMovie and Keynote editable videos from png files

Lets say you have a collection of *.png files: 0001.png, 0002.png, 0003.png and so on.
These *.png files represent different frames of a movie you just rendered (in Blender, Maya, or some other program). How do you actually combine these into a movie?
One way is to use the following script:

ffmpeg -i %4d.png -c:v libx264 -r 24 myvideo.mp4

Which will render the simulation at 24 frames per second in the libx264 codec (and it looks very nice!). However, the libx264 codec is not compatible with Quicktime, iMovie, or even Keynote (you can play it on videolan though).
It is better to use the mpeg4 codec which is readily editable in either of these:

ffmpeg -r 24 -i %4d.png -c:v mpeg4  -vb 20M myvideo.mp4

Update: ffmpeg complained sometimes on the .png files not being found. The following command works better:

ffmpeg -r 48 -pattern_type glob -i '*.png'   -c:v mpeg4  -vb 20M myvideo.mp4

Best way of finding differences (diff) between two files on Mac OS X

Hello readers,

I wanted to share with you a great tool available on the Mac for reviewing code differences. If you have ever used the terminal in Mac or Linux, the most straight forward to view code differences is to use ‘diff’:

diff file1 file2

Screen Shot 2018-01-31 at 7.06.43 PM.png

The problem with this approach is that it is not easy to discern what the actual differences are from the output if the file is large, and there are many differences.

It is much easier to use ‘opendiff’:

opendiff file1 file2

Which will give you a very nice graphical visualization of the differences:

Screen Shot 2018-01-31 at 7.44.21 PM.png

Taken from here.

However, sometimes opendiff fails. In that case, I recommend paying for Beyond Compare, which is really the best diff software I encountered so far.