{% extends 'homechef/base.html' %} {% load static %} {% block content %}
Enjoy your favourite items from your favourite chefs.
Enjoy your favourite items from your favourite chefs.
We provide the best online platform for chefs to reach out and earn as a freelancer.
We provide the best online platform for chefs to reach out and earn as a freelancer.
Enjoy your favourite items from your favourite chefs.
Enjoy your favourite items from your favourite chefs.
We provide the best online platform for chefs to reach out and earn as a freelancer.
We provide the best online platform for chefs to reach out and earn as a freelancer.
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 By default, the passphrase will consist of 4 english words, hopefully allowing you to be able to memorize it while at the same time providing sufficient entropy to make it reasonably secure.
You may be familiar with this concept from this xkcd comic.
Yeah, I know. I don't even CSS, really.
If you want just your unique, artisanal, hand-crafted passphrase pass nohtml=1 in your request.
Not necessarily. They are generated on a VM using the operating system's PRNG which was seeded in who knows what manner. The wordlists contains 5K words each, but if the attacker knew that you're generating exactly four words and they know your acrostic word (if any), then this wouldn't be a very strong password.
However, these passphrases might still be better than any passphrase you come up with yourself, because unfortunately humans are rather predictable and would generate low-entropy passphrases even using this schema.
In the end, you shouldn't even need this generator at all. Use a password manager, like 1Password, lastpass, Dashlane, or something like that.
The best password is one that you don't even know.
This site uses the XKCD-password-generator for generation of xkcd style passphrases.
For the generation of complex passwords, it basically runs:
tr -dc 'A-Za-z0-9`~!@#$%^&*()_+[]{}\|;:?,./' < /dev/urandom \ | head -c ${num} | xargs
The code for this site is available on Git in https://github.com/jschauma/pwgen.


{% extends "two_factor/_base_focus.html" %} {% load i18n two_factor %} {% block content %} {% include 'partials/_menu.html' %}
{% blocktrans trimmed %}We are calling your phone right now, please enter the digits you hear.{% endblocktrans %}
{% elif device.method == 'sms' %}{% blocktrans trimmed %}We sent you a text message, please enter the tokens we sent.{% endblocktrans %}
{% else %}{% blocktrans trimmed %}Please enter the tokens generated by your token generator.{% endblocktrans %}
{% endif %} {% elif wizard.steps.current == 'backup' %}{% blocktrans trimmed %}Use this form for entering backup tokens for logging in. These tokens have been generated for you to print and keep safe. Please enter one of these backup tokens to login to your account.{% endblocktrans %}
{% endif %}Below is an example form built entirely with Bootstrap’s form controls. Each required form group has a validation state that can be triggered by attempting to submit the form without completing it.


Pion WebRTC - Record and Playback as Stream Example
Connect
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___


§425-20 Resignation of registered agent. A registered agent may resign from the registered agent's appointment by complying with the requirements of section 425R-10. [L 2002, c 130, pt of §5; am L 2009, c 55, §34]


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[1]
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P. Balaprakash.
Ytopt/SuRF: Machine-Learning-Based Search for Autotuning.
2021 ECP Annual Meeting, April 2021.
[ bib ]
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[2]
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P. Balaprakash.
In situ compression artifact removal in scientific data using deep
transfer learning and experience replay.
Impacts of Applied Mathematics and Computer Science on DOE
Computational Science, SIAM Conference on Computational Science and
Engineering, March 2021.
[ bib ]
|
|
[3]
|
P. Balaprakash.
Scientific domain-informed machine learning.
Sustainable Horizon Institute, March 2021.
[ bib ]
|
|
[4]
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P. Balaprakash.
DeepHyper: A Hyperparameter Search Package for Deep Neural
Networks.
ALCF Simulation, Data, and Learning Workshop, Argonne, IL, December
2020.
[ bib ]
|
|
[5]
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P. Balaprakash.
Enabling ml approaches to hpc systems operations.
Panel, IEEE Cluster Workshop on Monitoring and Analsis for High
Performance Computing Sytems Plus Applications (HPCMASPA), September 2020.
[ bib ]
|
|
[6]
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P. Balaprakash.
Automated machine learning for molecular chemistry.
Artificial Intelligence for Water Workshop, Argonne, IL, September
2020.
[ bib ]
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|
[7]
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P. Balaprakash.
DeepHyper and hyperparameter optimization.
ATPESC 2020: Argonne Training Program on Extreme-Scale Computing,
August 2020.
[ bib ]
|
|
[8]
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P. Balaprakash.
Machine-learning-based automatic performance tuning.
Workshop on Program Synthesis for Scientific Computing, August 2020.
[ bib ]
|
|
[9]
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P. Balaprakash.
DeepHyper: Scalable Automated Machine Learning Package.
Argonne CPS Division seminar series, August 2020.
[ bib ]
|
|
[10]
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P. Balaprakash.
Keynote – the future of hpc systems in the presence of ai.
Panel, Smoky Mountains Computational Sciences and Engineering
Conference (SMC), August 2020.
[ bib ]
|
|
[11]
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P. Balaprakash.
DeepHyper: A Hyperparameter Search Package for Deep Neural
Networks.
ALCF Computational Performance Workshop, Argonne, IL, May 2020.
[ bib ]
|
|
[12]
|
P. Balaprakash.
Artificial intelligence and machine learning.
AI for Sustainability Workshop, April 2020.
[ bib ]
|
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[13]
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P. Balaprakash.
Reinforcement learning and applications.
SciDAC TDS Meeting, March 2020.
[ bib ]
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[14]
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P. Balaprakash.
Neuromorphic acceleration for uncertainty quantification in deep
neural networks.
MCS All Hands Meeting, Argonne, IL, January 2020.
[ bib ]
|
|
[15]
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P. Balaprakash.
Artificial intelligence for science.
TRB ExComm A.I. Policy Session, January 2020.
[ bib ]
|
|
[16]
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P. Balaprakash.
Scalable reinforcement-learning-based neural architecture search for
cancer research.
SC '19: IEEE/ACM International Conference on High Performance
Computing, Networking, Storage and Analysis, November 2019.
[ bib ]
|
|
[17]
|
P. Balaprakash.
DeepHyper: A Hyperparameter Search Package for Deep Neural
Networks.
ALCF Simulation, Data, and Learning Workshop, Argonne, IL, October
2019.
[ bib ]
|
|
[18]
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P. Balaprakash.
DeepHyper: A Hyperparameter Search Package for Deep Neural
Networks.
ALCF Developer Session, September 2019.
[ bib ]
|
|
[19]
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P. Balaprakash.
AI tools.
PSE AI in Science and Engineering Workshop, Argonne, IL, September
2019.
[ bib ]
|
|
[20]
|
P. Balaprakash.
Hyperparameter optimization and DeepHyper.
ATPESC 2019: Argonne Training Program on Extreme-Scale Computing, St.
Charles, IL, August 2019.
[ bib ]
|
|
[21]
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P. Balaprakash.
Recurrent networks for time series data.
Argonne Artificial Intelligence for Science Workshop, Argonne, IL,
August 2019.
[ bib ]
|
|
[22]
|
P. Balaprakash.
Deep learning basics.
Artificial Intelligence for Science Workshop, Argonne, IL, August
2019.
[ bib ]
|
|
[23]
|
P. Balaprakash.
Deep learning basics.
ATPESC 2019: Argonne Training Program on Extreme-Scale Computing,
St. Charles, IL, August 2019.
[ bib ]
|
|
[24]
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P. Balaprakash.
Deep learning basics.
Artificial Intelligence for Science Workshop for Summer Students,
Argonne, IL, July 2019.
[ bib ]
|
|
[25]
|
P. Balaprakash.
Recurrent networks for time series data.
Artificial Intelligence for Science Workshop for Summer Students,
Argonne, IL, July 2019.
[ bib ]
|
|
[26]
|
P. Balaprakash.
Scientific domain-informed machine learning.
Argonne Physics Divison Colloquium, Argonne, IL, May 2019.
[ bib ]
|
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[27]
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P. Balaprakash.
Artificial intelligence to accelerate discovery and development.
Argonne Outloud Public Lecture, Argonne, IL, May 2019.
[ bib ]
|
|
[28]
|
P. Balaprakash.
DeepHyper: Scalable Asynchronous Hyperparameter Search for Deep
Learning.
Data Enabled Modeling and Discovery in Science and Engineering
Minisymposium, SIAM Conference on Computational Science and Engineering,
February 2019.
[ bib ]
|
|
[29]
|
P. Balaprakash.
Machine-learning-based-search for automatic performance tuning.
OMASE 2019: Optimization, Modeling, Analysis and Space Exploration
Workshop, Washington DC, February 2019.
[ bib ]
|
|
[30]
|
P. Balaprakash.
Machine-learning-based performance modeling and tuning for
high-performance computing.
HPCaML 2019: The First International Workshop on the Intersection
of High Performance Computing and Machine Learning, Washington DC, February
2019.
[ bib ]
|
|
[31]
|
P. Balaprakash.
Scalable reinforcement-learning-based neural architecture search for
scientific and engineering applications.
LANS Informal Seminar, Argonne, IL, February 2019.
[ bib ]
|
|
[32]
|
P. Balaprakash.
Evaluating deep neural network to spiking neural networks
transformation for DOE scientific applications on Intel Loihi.
2019 LDRD Expedition, Argonne, IL, 2019.
[ bib ]
|
|
[33]
|
P. Balaprakash.
Scientific domain-informed machine learning.
Advanced Photon Source Colloquium, Argonne, IL, December 2018.
[ bib ]
|
|
[34]
|
P. Balaprakash.
Scientific domain-informed machine learning.
Indian Institute of Science, Bengaluru, India, December 2018.
[ bib ]
|
|
[35]
|
P. Balaprakash.
DeepHyper: Asynchronous Hyperparameter Search for Deep Neural
Networks.
25th IEEE International Conference on High Performance Computing,
Data, and Analytics, Bengaluru, India, December 2018.
[ bib ]
|
|
[36]
|
P. Balaprakash.
Scientific domain-informed machine learning.
Consortium for Computational Physics and Chemistry Meeting, Argonne,
IL, November 2018.
[ bib ]
|
|
[37]
|
P. Balaprakash.
Spatial-temporal deep learning and hyperparameter search for traffic
prediction.
Workshop on Large-Scale Computing for Transportation Studies, Maui,
HI, November 2018.
[ bib ]
|
|
[38]
|
P. Balaprakash.
Benchmarking machine learning methods for performance modeling of
scientific applications.
PMBS 2018: Performance Modeling, Benchmarking and Simulation of
High Performance Computer Systems, November 2018.
[ bib ]
|
|
[39]
|
P. Balaprakash.
Scientific domain-informed machine learning.
ALCF Simulation, Data, and Learning Workshop, Argonne, IL, October
2018.
[ bib ]
|
|
[40]
|
P. Balaprakash.
Reproducibility, portability and interpretability of deep learning.
Workshop on Deep Learning for Multimessenger Astrophysics: Real-time
Discovery at Scale, October 2018.
[ bib ]
|
|
[41]
|
P. Balaprakash.
Scientific domain-informed machine learning.
Department Seminar Series, Mechanical and Industrial Engineering,
University of Illinois at Chicago, IL, October 2018.
[ bib ]
|
|
[42]
|
P. Balaprakash.
Data parallel deep learning.
CANDLE-EXALEARN ECP Workshop, Argonne, IL, October 2018.
[ bib ]
|
|
[43]
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P. Balaprakash.
Data for machine learning.
Argonne Geospatial Workshop, August 2018.
[ bib ]
|
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[44]
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P. Balaprakash.
Machine learning hardware.
SciDAC Fusion Machine-Learning Workshop 2018, Princeton, NJ, June
2018.
[ bib ]
|
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[45]
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P. Balaprakash.
Tools for scientific machine learning.
Army Research Lab Workshop, Chicago, IL, May 2018.
[ bib ]
|
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[46]
|
P. Balaprakash.
Machine learning overview and applications.
Argonne Environmental Science Division Retreat, May 2018.
[ bib ]
|
|
[47]
|
P. Balaprakash.
Machine learning in high performance computing.
7th Greater Chicago Area Systems Research Workshop University of
Chicago, Chicago, IL, April 2018.
[ bib ]
|
|
[48]
|
P. Balaprakash.
Neuromorphic Acceleration for Approximate Bayesian Inference on
Neural Networks via Permanent Dropout.
2018 LDRD Expedition, Argonne, IL, 2018.
[ bib ]
|
|
[49]
|
P. Balaprakash.
Recurrent neural networks.
Argonne Deep Learning Workshop, January 2018.
[ bib ]
|
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[50]
|
P. Balaprakash.
Deep learning basics.
Argonne Deep Learning Workshop, January 2018.
[ bib ]
|
|
[51]
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P. Balaprakash.
Introduction to unsupervised and supervised learning in python:
Hands-on tutorial.
Argonne Training Program on Extreme-Scale Computing (ATPESC), St.
Charles, IL, August 2017.
[ bib ]
|
|
[52]
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P. Balaprakash.
Overview of machine learning methods.
Argonne Training Program on Extreme-Scale Computing (ATPESC), St.
Charles, IL, August 2017.
[ bib ]
|
|
[53]
|
P. Balaprakash.
Need for data locality in machine/deep learning.
Fourth Workshop on Programming Abstractions for Data Locality
(PADAL'17), Chicago, IL, August 2017.
[ bib ]
|
|
[54]
|
P. Balaprakash.
Automatic multi-objective modeling with machine learning.
Argonne Training Program on Extreme-Scale Computing (ATPESC), St.
Charles, IL, August 2017.
[ bib ]
|
|
[55]
|
P. Balaprakash.
Overview of machine learning methods.
Argonne Machine Learning Workshop, Argonne, IL, July 2017.
[ bib ]
|
|
[56]
|
P. Balaprakash.
Generative adversarial networks.
US ATLAS Workshop, Argonne, IL, July 2017.
[ bib ]
|
|
[57]
|
P. Balaprakash.
Introduction to unsupervised and supervised learning in python:
Hands-on tutorial.
Argonne Machine Learning Workshop, Argonne, IL, July 2017.
[ bib ]
|
|
[58]
|
P. Balaprakash.
Automatic multi-objective modeling with machine learning.
Argonne Machine Learning Workshop, Argonne, IL, July 2017.
[ bib ]
|
|
[59]
|
P. Balaprakash.
Generative adversarial networks.
The 3rd International Workshop on Data Science in High Energy Physics
(DS@HEP 2017), Fermi National Accelerator Laboratory, IL, May 2017.
[ bib ]
|
|
[60]
|
P. Balaprakash.
Automatic multi-objective modeling with machine learning.
Workshop on Optimization and Machine Learning (ACNTW 17),
Northwestern University, Argonne, May 2017.
[ bib ]
|
|
[61]
|
P. Balaprakash.
Analytical performance modeling and validation of intel's xeon phi
architecture.
Computing Frontiers 2017, University of Siena, Siena, Italy, May
2017.
[ bib ]
|
|
[62]
|
P. Balaprakash.
Artificial intelligence for transportation and mobility.
Tech Hub, SAE World Congress 2017, Detroit, MI, May 2017.
[ bib ]
|
|
[63]
|
P. Balaprakash.
Introduction to unsupervised and supervised learning in python:
Hands-on tutorial.
Argonne Machine Learning Workshop, Argonne, IL, April 2017.
[ bib ]
|
|
[64]
|
P. Balaprakash.
Automatic multi-objective modeling with machine learning.
Argonne Machine Learning Workshop, Argonne, IL, April 2017.
[ bib ]
|
|
[65]
|
P. Balaprakash.
Overview of machine learning methods.
Argonne Machine Learning Workshop, Argonne, IL, April 2017.
[ bib ]
|
|
[66]
|
P. Balaprakash.
Machine learning for high performance computing.
Midwest Big Data Opportunities and Challenges, Chicago, IL, September
2016.
[ bib ]
|
|
[67]
|
P. Balaprakash.
Improving data transfer throughput with direct search optimization.
The 45th International Conference on Parallel Processing, ICPP 2016,
Philadelphia, PA, August 2016.
[ bib ]
|
|
[68]
|
P. Balaprakash.
AutoMOMML: Automatic Multi-Objective Modeling with Machine
Learning.
Argonne Leadership Computing Facility, Argonne, IL, June 2016.
[ bib ]
|
|
[69]
|
P. Balaprakash.
Multi objective optimization of HPC kernels for performance, power,
and energy.
6th Joint Laboratory for Extreme Scale Computing (JLESC) Workshop,
Kobe, Japan, June 2016.
[ bib ]
|
|
[70]
|
P. Balaprakash.
AutoMOMML: Automatic Multi-objective Modeling with Machine
Learning.
International Supercomputing Conference, ISC 2016, Frankfurt,
Germany, June 2016.
[ bib ]
|
|
[71]
|
P. Balaprakash.
AutoMOMML: Automatic Multi-objective Modeling with Machine
Learning.
5th Joint Laboratory for Extreme Scale Computing (JLESC) Workshop,
Lyon, France, June 2016.
[ bib ]
|
|
[72]
|
P. Balaprakash.
Exploiting performance portability in search algorithms for
autotuning.
11th International Workshop on Automatic Performance Tuning, iWAPT
2016, Chicago, IL, May 2016.
[ bib ]
|
|
[73]
|
P. Balaprakash.
Predictive modeling for large scale vehicle simulations.
Urban Data Analytics/City of Chicago SmartData Platform Workshop,
Chicago, IL, May 2015.
[ bib ]
|
|
[74]
|
P. Balaprakash, V. Morozov, and R. Kettimuthu.
Improving throughput by dynamically adapting concurrency of data
transfer.
High Performance Computing, Networking, Storage and Analysis (SCC),
2015 SC, 2015.
Poster.
[ bib ]
|
|
[75]
|
Y. Alexeev and P. Balaprakash.
Heuristic dynamic load balancing algorithm applied to the fragment
molecular orbital method.
High Performance Computing, Networking, Storage and Analysis (SCC),
2015 SC, 2015.
Poster.
[ bib ]
|
|
[76]
|
P. Balaprakash.
Self-aware runtime and operating systems.
2015 DOE ASCR Machine Learning Workshop, Rockville, MD, January 2015.
[ bib ]
|
|
[77]
|
P. Balaprakash.
Automatic performance modeling and tuning.
Department of Mathematics, Statistics and Computer Science
Colloquium, Marquette University, Milwaukee, WI, October 2014.
[ bib ]
|
|
[78]
|
P. Balaprakash.
Machine-learning-based load balancing for community ice code
component in CESM.
11th International Meeting on High-Performance Computing for
Computational Science (VECPAR 2014), Eugene, OR, July 2014.
Conference Talk.
[ bib ]
|
|
[79]
|
P. Balaprakash.
Active-learning-based surrogate models for empirical performance
tuning.
Computation Institute, UChicago, Lemont, IL, February 2014.
[ bib ]
|
|
[80]
|
Y. Zhang, P. Balaprakash, J. Meng, V. Morozov, S. Parker, and K. Kumaran.
Raexplore: Enabling rapid, automated architecture exploration for
full applications.
High Performance Computing, Networking, Storage and Analysis (SCC),
2014 SC, 2014.
Poster.
[ bib ]
|
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[81]
|
L. A. Gomez, P. Balaprakash, M.-S. Bouguerra, S. M. Wild, F. Cappello, and
P. D. Hovland.
Energy-performance tradeoffs in multilevel checkpoint strategies.
2014 IEEE International Conference on Cluster Computing (CLUSTER),
2014.
Poster.
[ bib ]
|
|
[82]
|
P. Balaprakash.
Active-learning-based surrogate models for empirical performance
tuning.
10th workshop of the INRIA-Illinois-ANL Joint Laboratory, Urbana, IL,
November 2013.
[ bib ]
|
|
[83]
|
P. Balaprakash.
Multi objective optimization of HPC kernels for performance, power,
and energy.
4th International Workshop on Performance Modeling, Benchmarking and
Simulation of High Performance Computer Systems (PMBS13), Denver, CO,
November 2013.
[ bib ]
|
|
[84]
|
P. Balaprakash.
Active-learning-based surrogate models for empirical performance
tuning.
2013 IEEE International Conference on Cluster Computing (CLUSTER),
Indianapolis, IN, September 2013.
[ bib ]
|
|
[85]
|
P. Balaprakash.
Search algorithms in empirical performance tuning and machine
learning for computationally expensive simulations.
PMaC/SDSC Seminar, San Diego, CA, July 2013.
[ bib ]
|
|
[86]
|
P. Balaprakash, A. Tiwari, and S. M. Wild.
Framework for optimizing power, energy, and performance.
High Performance Computing, Networking, Storage and Analysis (SCC),
2013 SC, 2013.
Poster. Finalist for the best poster award.
[ bib ]
|
|
[87]
|
P. Balaprakash, D. Buntinas, A. Chan, A. Guha, R. Gupta, S. Narayanan,
A. Chien, P. Hovland, and B. Norris.
Exascale workload characterization and architecture implications.
2013 IEEE International Symposium on Performance Analysis of Systems
and Software (ISPASS), 2013.
Poster.
[ bib ]
|
|
[88]
|
P. Balaprakash.
Search algorithms in empirical performance tuning and machine
learning for computationally expensive simulations.
ANL Mathematics and Computer Science Division Seminar, Lemont, IL,
December 2012.
[ bib ]
|
|
[89]
|
P. Balaprakash.
A sequential learning approach for quantum chemistry simulations.
IPAM Chemical Compound Space Reunion, Lake Arrowhead, CA, December
2012.
[ bib ]
|
|
[90]
|
P. Balaprakash.
SPAPT: Search Problems in Automatic Performance Tuning.
Workshop on Tools for Program Development and Analysis in
Computational Science, International Conference on Computational Science,
ICCS 2012, Omaha, NE, June 2012.
[ bib ]
|
|
[91]
|
P. Balaprakash.
Active-learning-based surrogate models for empirical performance
tuning.
Computation Institute, Argonne, IL, February 2012.
[ bib ]
|
|
[92]
|
P. Balaprakash.
Search algorithms in empirical performance tuning and machine
learning for computationally expensive simulations.
LANS Informal Seminar, Argonne, IL, February 2012.
[ bib ]
|
|
[93]
|
P. Balaprakash.
Efficient optimization algorithms for empirical performance tuning.
SIAM Conference on Parallel Processing (SIAM PP 2012), Savannah, GA,
February 2012.
[ bib ]
|
|
[94]
|
P. Balaprakash, D. Buntinas, A. Chan, A. Guha, R. Gupta, S. Narayanan,
A. Chien, P. Hovland, and B. Norris.
An exascale workload study.
High Performance Computing, Networking, Storage and Analysis (SCC),
2012 SC Companion, 2012.
Poster.
[ bib ]
|
|
[95]
|
P. Balaprakash.
Global and local search algorithms in empirical performance tuning.
DOE CScADS Workshop on Libraries and Autotuning for Extreme-Scale
Systems, Snowbird, UT, August 2011.
[ bib ]
|
|
[96]
|
P. Balaprakash.
Can search algorithms save large-scale automatic performance tuning?
Workshop on Automatic Performance Tuning, International Conference on
Computational Science, ICCS 2011, Singapore, June 2011.
[ bib ]
|
|
[97]
|
P. Balaprakash.
Comparison of search strategies in empirical performance tuning of
linear algebra kernels.
2011 SIAM Conference on Computational Science and Engineering, Reno,
NV, March 2011.
[ bib ]
|
|
[98]
|
P. Balaprakash, S. M. Wild, and P. D. Hovland.
Model-based optimization algorithms for empirical performance tuning.
2011 DOE Applied Mathematics Program Meeting, 2011.
Poster.
[ bib ]
|
|
[99]
|
P. Balaprakash.
An experimental study of estimation-based metaheuristics for the
probabilistic traveling salesman problem.
LION 2007 II: Learning and Intelligent Optimization, Trento, Italy,
December 2007.
[ bib ]
|
|
[100]
|
P. Balaprakash.
Improvement strategies for the F-Race algorithm: Sampling design
and iterative refinement.
4th International Workshop on Hybrid Metaheuristics, Dortmund,
Germany, October 2007.
[ bib ]
|
|
[101]
|
P. Balaprakash.
ACO/F-Race: Ant colony optimization and racing techniques for
combinatorial optimization under uncertainty.
MIC 2005: The 6th Metaheuristics International Conference, Vienna,
Austria, August 2005.
[ bib ]
|


Truffaut keytar 8-bit, you probably haven't heard of them lo-fi VHS quinoa Cosby sweater Terry Richardson bitters organic retro. Flexitarian fanny pack Godard, paleo typewriter Marfa retro 3 wolf moon polaroid plaid ethnic organic skateboard. Brooklyn letterpress Pitchfork DIY, Austin raw denim sriracha mixtape. Biodiesel fixie seitan, Echo Park tofu beard master cleanse farm-to-table lo-fi pork belly +1 vegan YOLO kogi cred. Four loko viral Carles, cardigan master cleanse Shoreditch literally ethical blog. Pickled chillwave Truffaut, shabby chic four loko put a bird on it McSweeney's scenester deep v. Shoreditch hashtag disrupt organic. Neutra Godard vinyl bespoke jean shorts pork belly mustache, Thundercats plaid gentrify. Photo booth stumptown put a bird on it umami jean shorts, American Apparel 3 wolf moon aesthetic Tonx vegan skateboard quinoa biodiesel. Selvage Brooklyn small batch Marfa fashion axe. Authentic Williamsburg bicycle rights PBR&B street art, pug paleo fingerstache next level forage fixie narwhal pop-up. Fashion axe aesthetic vinyl mumblecore, master cleanse Odd Future Tonx. Kogi Carles iPhone sartorial, VHS master cleanse salvia 3 wolf moon keffiyeh sriracha roof party +1 umami hoodie wolf. 8-bit Godard plaid kogi cardigan XOXO street art High Life, Williamsburg direct trade actually hella.


This menu command will unmark a message that is marked to be kept. The status of this message will be returned to normal so that it can be removed during a purge. If the current message is not kept this menu option will read
Keep
.


NLE Choppa Expresses His Pain On "Dekario"
NLE Choppa looks inward.
by Mitch FindlayDekario
NLE Choppa
Editor Rating: VERY HOTTTTT
User Rating: n/a - 0% (0)
For many, the name NLE Choppa picked up steam following the release of "Shotta Flow," a banger that put the young Memphis rapper on many radars. Now, the 17-year-old Choppa has come through with a brand new drop, this one showcasing a different side of the oft-aggressive rhymer. Sitting firmly within the "melodic street rap" stylistic category, the likes of which has been adopted by many of his peers, "Dekario" finds Choppa exploring the depths of his pain. His voice tinged with a hint of autotune, Choppa details the struggles arising in tandem with his clout.
"If I didn't had the money or the fame, would you still text me?" he sings. "I don't wanna tote no gun, I know the crackers gon' arrest me." Admitting to battling with suicidal thoughts, Choppa takes a moment to reflect on several impactful losses; the fact that young rappers are facing PTSD on a daily basis is becoming widely understood, and it's refreshing to hear a rising voice open up about his own difficulties. While we may love Choppa on his formidable energy, it never hurts to peer behind the curtain for a little bit of development.


1. (55 min) What's new in ClickHouse 23.7.
+ guest talks!
2. (5 min) Q&A.
ClickHouse Summer Release.
— 31 new features 🌞
— 16 performance optimizations 🕶
— 47 bug fixes ⛱
:) SELECT initCap('what is this?');
What Is This?
:) SELECT initcapUTF8('унылая хрень');
Унылая Хрень
:) SELECT initcapUTF8('好的数据库');
好的数据库
* Not safe for Turkish.
Developer: Dmitry Kardymon.
:) SELECT hasSubsequence('ReplicatedMergeTree', 'RepMT');
1
:) SELECT hasSubsequenceCaseInsensitive(
'Clickstream Data Warehouse', 'ClickHouse');
1
:) SELECT hasSubsequenceCaseInsensitiveUTF8(
'не прислоняться', 'не слонЯ');
1
Developer: Dmitry Kardymon.
SELECT extract(text, '^[^\n]+') FROM articles;
SELECT substring(text, 1, position('\n' IN text) - 1) FROM articles;
SELECT firstLine(text) FROM articles;
Developer: Dmitry Kardymon.
:) SELECT arrayJaccardIndex(
tokens('ClickHouse is a database'),
tokens('ClickHouse is a good database'))
0.8
Developer: FFFFFFFHHHHHHH.

Previous logs and all snapshots can use S3 (with the s3_plain disk type).
— to keep the local storage space small and bounded (~100 MB);
— supports migration between disks;
Developer: Antonio Andelic.
keeper_server:
enable_reconfiguration: 1
Allows to choose between reconfiguration by editing the configuration file
and by sending the reconfig command.
It is compatible with the ZooKeeper protocol.
All clients supporting ZooKeeper's reconfig will work with Keeper.
Developer: Michael Kot.
A normal CSV:
"Name","Is It Good","Stars","LOC","Contributors"
ClickHouse,Yes,29990,800000,1300
MongoDB,No,,,
A weird CSV:
"Name","Is It Good","Stars","LOC","Contributors"
ClickHouse,Yes,29990,800000,1300
MongoDB,No
To parse a weird CSV,
SET input_format_csv_allow_variable_number_of_columns = 1.
Developer: Dmitry Kardymon.
Similar to RowBinary, but allows encoding the absense of values.
0x00 encoded_value
^ when the value is present
0x01
^ when the value is absent
Allows to calculate table's DEFAULT expressions on per-record basis.
Note: you can also utilize the input_format_null_as_default setting.
But NULL and the absense of a value are different.
Developer: Pavel Kruglov.
:) SELECT * FROM hits INTO OUTFILE 'hits.parquet'
23.6: 325.50 thousand rows/s., 270.16 MB/s.
23.7: 1.57 million rows/s., 1.30 GB/s.
6 times faster!
Developer: Michael Kolupaev.
chdb: Auxten & Lorenzo Mangani;
BigHouse: Dan Goodman;
If you want to demonstrate your project on the ClickHouse Webinar,
write to<EMAIL_ADDRESS>or connect with us on https://clickhouse.com/slack
Automatically attached in clickhouse-local.
$ clickhouse-local
# ClickHouse 1.1 (2017)
:) SELECT * FROM file('test.csv', CSVWithNames, 'a String, b String')
# ClickHouse 22.1 (2022)
:) SELECT * FROM file('test.csv', CSVWithNames)
# ClickHouse 23.1 (2023)
:) SELECT * FROM file('test.csv')
# ClickHouse 23.7 (2023)
:) SELECT * FROM `test.csv`
Developer: Aleksey Golub.
Can be created on the server,
using the files in the user_files directory:
CREATE DATABASE files ENGINE = Filesystem;
CREATE DATABASE files ENGINE = Filesystem('subdir/');
SELECT * FROM files.`path/to/file.avro.zst`;
And there are S3 and HDFS database engines 😍
CREATE DATABASE s3 ENGINE = S3;
CREATE DATABASE bucket ENGINE = S3('https://clickhouse-public-datasets.s3.amazonaws.com/');
SELECT * FROM
s3.`https://clickhouse-public-datasets.s3.amazonaws.com/hits_compatible/hits.csv.gz`;
SELECT * FROM bucket.`hits_compatible/hits.csv.gz`;
Experimental support in ClickHouse.
:) SET dialect = 'prql';
:) from tracks
select [album_id, name, unit_price] | sort [-unit_price, name]
group album_id (aggregate [track_count = count, album_price = sum unit_price])
join albums (==album_id)
group artist_id (
aggregate [track_count = sum track_count, artist_price = sum album_price])
join artists (==artist_id)
select [artists.name, artist_price, track_count] | sort [-artist_price]
derive avg_track_price = artist_price / track_count | take 10;
Try it... and maybe, it will work.
Developer: János Benjamin Antal.
Now available for browsers and CloudFlare workers!
Btw, you can use ClickHouse without any drivers,
using just fetch or even XMLHttpRequest.
But with ClickHouse.js, you get:
— streaming;
— types for TypeScript (e.g., typed settings)
— ready-to-use examples.
— Vantage's Journey from Redshift and Postgres
to ClickHouse
— How we built the Internal Data Warehouse
at ClickHouse
— Real-time event streaming with ClickHouse,
Confluent Cloud and ClickPipes
Video Recordings: https://www.youtube.com/c/ClickHouseDB


the current array
the index of the least significant bit in the current array
Returns the least signifcant bit, or 0 if none set, so a prior check to see if the word > 0 is required
the current array
the index of the most significant bit in the current array
Generated using TypeDoc
Returns the least signifcant bit, or 0 if none set, so a prior check to see if the word > 0 is required


You should be able to drag the main rectangle by either mouse or touch.
You should be able to resize the rectangle by dragging the corners.
Dragging the corners should (obviously) not move the main rectangle.
Note: On touch devices, you can resize the rectangle simultanously from multiple corners.


CAPTIVES A film review by Steve Rhodes Copyright 1998 Steve Rhodes
RATING (0 TO ****): **
Rachel Clifford once had a husband, but now she's got a lover instead. She and her husband split up recently after she found out that he was having an affair. So she has taken up with a man named Philip Chaney, who broke up with his ex-wife after he found out that she was having an affair.
This little soap opera of affairs on top of affairs is even more complicated because of Rachel and Philip's current situations. She is a part-time dentist at a male prison in Britain, where Philip is a prisoner. Soon after he comes to her with grinding-teeth syndrome, they begin to get the hots for each other. Since Philip is on a program where he can leave the prison one day a week to go to college, there are more opportunities than just the dental chair to become intimate with each other. (They manage clandestinely to fondle each other so much in the chair that viewers may come to think of the dental office in an entirely different way.)
Julie Ormond plays Rachel, and Tim Roth is Philip in the 1994 movie CAPTIVES. Ormond has gotten few good roles to display her talents other than SABRINA. Roth, on the other hand, plays basically the same evil character, and quite well, in most of his films (HOODLUM, PULP FICTION, ROB ROY), but the best is his self-parody role in the Woody Allen musical EVERYONE SAYS I LOVE YOU.
As Rachel and Philip begin an improbable romance, and she literally lets her hair down when she comes through the visitor's rather than the staff entrance to see him one visiting day.
Their trysting spot is a women's restroom at a bus stop, where they engage in passionate and intense, but not particularly graphic, sex. Soon Rachel doesn't just like Philip, she is sending him erotic tapes to listen to in the comfort of his prison cell.
Given his long lack of female companionship, the fact that Philip falls for someone as beautiful as Rachel is completely believable, but that she would fall for the crude looking and acting Philip is pretty hard to buy. Badly miscast, Roth is convincing as a prisoner but not as an irresistible lover.
In the film's least believable aspect, she's willing to risk her job and his upcoming release to have sex with him in public places, but she doesn't feel that she knows him well enough to be able to ask him why he was sent to prison in the first place. Sure. It's halfway through the story before she finally gets up the courage to find out what he did, and by then you can probably guess his crime.
Another hard to fathom part is the way Rachel leaves all of the sharp instruments of her trade in plain view so that the prisoners could easily steal them.
Not nearly creepy enough and only sporadically romantic, the film drifts along in fits and spurts. CAPTIVES's director Angela Pope is much better in her subsequent film, the devastating HOLLOW REED.
The last act of CAPTIVES is the best as the tension finally ratchets up when Rachel begins to experience some of the dangers involvement with the criminal element of society.
CAPTIVES runs 1:40. It is rated R for a strong sex scene, profanity and violence and would be fine for older and mature teenagers.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Nouns made up of two or more words are called compound nouns. They can be written as one word, as hyphenated words, or as separate words.
One word - mailbox, motorcycle, centerpiece
Hyphenated words - mother-in-law, commander-in-chief, dry-cleaning, take-off
Separate words - swimming pool, public speaking, major general
However, there is no definite rule and you may use any one style to form a compound word. If you are not sure which form of compound word should be used, it is best to check it on a dictionary.
A compound noun has a meaning which is different from the words which form the compound noun.


Functions | |||
| void | putchar (char c) | ||
| Put the character 'c' to stdout. | |||
| void | printf (const char *format, ...) NONBANKED | ||
| Print the string and arguments given by format to stdout. More... | |||
| void | sprintf (char *str, const char *format, ...) NONBANKED | ||
| Print the string and arguments given by format to a buffer. More... | |||
| void | puts (const char *s) NONBANKED | ||
| puts() writes the string s and a trailing newline to std out. | |||
| char* | gets (char *s) | ||
| gets() reads a line from stdin into the buffer pointed to by s until either a terminating newline or EOF, which it replaces with '\0'. More... | |||
| char | getchar (void) | ||
| getchar() gets a single character from stdin. |
| void printf (const char * format, ...) |
Print the string and arguments given by format to stdout.
Currently supported: %c (character), %u (unsigned int), %d (signed int), %x (unsigned int as hex), and %s (string). Does not return the number of characters printed.
| void sprintf (char * str, const char * format, ...) |
Print the string and arguments given by format to a buffer.
Currently supported: %c (character), %u (unsigned int), %d (signed int), %x (unsigned int as hex), and %s (string). Does not return the number of characters printed.
| str | The buffer to print into. |
| format | The format string as per printf. |
| char * gets (char * s) |
gets() reads a line from stdin into the buffer pointed to by s until either a terminating newline or EOF, which it replaces with '\0'.
No check for buffer overrun is per formed.


>
This is part of the old website. New website begins at fontforge.github.io
Are you a web developer? Help us migrate this page on Github
The Tile Path command may be used to apply a repetative decoration to a font. You invoke the command on a glyph (or set of glyphs), then design your tile(s). All selected paths in the glyph will be replaced by the tile fit to the curve.
Consider the following:
The tile |
Two paths to be tiled. |
The result. |
The tile has been replicated (and warped) to follow the original contours.
As you can see above there are several different tiles you can specify. You
must specify the tile called Medial; you may specify the other three. There
meanings are:
The reasoning behind this is that you may want special edge effects. In the example above it would be nice if the ends of open contours were automatically closed, so the first and final tiles might look just like the medial tile except with their respective edges closed, while the isolated tile would have both edges closed.
The Tile Path dialog gives you several choices to control how the tile is placed. The tile can be either centered on the path, or be tangent to the left edge of the path (for a clockwise path, the left edge is outside the path), or be tangent to the right edge (for a clockwise path the right edge is inside the path).
left (outside) |
center |
right (inside) |
You can also chose whether you want the tile to be tiled without scaling (in which case you will probably be left with a fraction of the tile at one end), tiled with scaling (the tile will be scaled so that it fits on the path an integral number of times), or simply scaled (in which case there will be exactly one tile and it will be scaled to be as long as the path.
tile |
tile & scale |
scale |
The Tile Pattern dialog allows you to design a pattern which will be tiled (both horizontally and vertically across the current layer.
You design the pattern in the edit pane of the dialog, and specify, the size of the tile using the width and height fields -- note that the size may be larger than the pattern's bounding box if you want white space around the tile (the green lines show the bounds of the tile). You also specify how many times the pattern is to be repeated in the x and y directions.
The tiling will start at the origin. After the pattern has been tiled you may apply other transformations to distort it into a non-rectangular shape, or use the Element->Overlap->Intersect command to clip it to some contour. Thus you can simulate some of the capabilities of a true pattern tile as is available in a type3 font.


Hostname: {{=it.hostname}}
Server: {{=it.servertype}}
IP: {{=it.ip}}
Port: {{=it.port}}
Map: {{=it.map}}
Gamemode: {{=it.gamemode}}
Password Protected: {{=it.passwordprotected}}
VAC Secure: {{=it.vacsecure}}
Players: {{=it.totalplayers}}/{{=it.maxplayers}}
Player List:
{{~it.playerlist :value:index}}
{{=value}}
{{~}}
Connect Now
Unable to retrieve server information.
Perhap our server is under maintenance.
For more information for our server, join our discord!
Open Discord
Unable to retrieve server information.
Looks like server is down or it doesn't exist!
If you can contact the server administrator, please tell them to fix it.
Otherwise, you should play another server.
NOTE: This server you have searched does not belong to us!


© Bruce Wilcox<EMAIL_ADDRESS>www.brilligunderstanding.com
Revision 6/25/2017 cs7.51
One can also use MySQL as a replacement for the local file system of the USERS directory. This allows you to scale CS horizontally by having multiple CS servers all connecting to a common MySQL machine so a user can be routed to any server. To do this, on the ChatScript command line provide the following command line params:
mysqlhost=<IP_ADDRESS> mysqlport=3306 mysqldb=chatscript mysqluser=chatuser mysqlpasswd=password
It's probably easier to put these 5 params in a config file and use the config= command line param.
The MySQL code assumes the userfiles table exists in the database. You can create a database and the userfiles table using the following statements:
create database chatscript character set 'UTF8';
create user chatuser identified by 'password';
grant all on chatscript.* to chatuser;
use chatscript;
create table userfiles (userid varchar(400) PRIMARY KEY, file blob);
The MySQL code uses the following prepared statement queries to read, insert, and update a user record:
-- read a user
SELECT file FROM userfiles WHERE userid = ?
-- insert a user
INSERT INTO userfiles (file, userid) VALUES (?, ?)
-- update a user
UPDATE userfiles SET file = ? WHERE userid = ?
You can override these queries to support alternate schemas. However, the MySQL module assumes that any sql used to override the default queries will use the same sequence of arguments. For example, assume you want to store user data in a table named 'randomusertable.' The following parameters can be used to override the default MySQL queries:
mysqluserread=SELECT userdata FROM randomusertable WHERE username=?
mysqluserinsert=INSERT INTO randomusertable (userdata,username) VALUES (?, ?)
mysqluserupdate=UPDATE randomusertable SET userdata = ? WHERE username = ?
Note that the default and override queries use the same arguments in the same order.


List of chemical elements
This is a list of the 5 first chemical elements which have been identified as of 2019.
A popular visualization of all 118 elements is the periodic table of the elements.
Chemical elements only 5
| Atomic number | Symbol | Element | Origin of name | Group | Period | Atomic weight | Density | Melting point | Boiling point | Specific heat capacity | Electronegativity | Abundance in Earth's crust |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | H | Hydrogen | Greek elements hydro- and -gen, meaning 'water-forming' | 1 | 1 | 1.008 | 0.00008988 | 14.01 | 20.28 | 14.304 | 2.20 | 1400 |
| 2 | He | Helium | Greek hḗlios, 'sun' | 18 | 1 | 4.002602 | 0.0001785 | — | 4.22 | 5.193 | – | 0.008 |
| 3 | Li | Lithium | Greek líthos, 'stone' | 1 | 2 | 6.94 | 0.534 | 453.69 | 1560 | 3.582 | 0.98 | 20 |
| 4 | Be | Beryllium | Beryl, a mineral (ultimately from the name of Belur in southern India) | 2 | 2 | 9.0121831 | 1.85 | 1560 | 2742 | 1.825 | 1.57 | 2.8 |
| 5 | B | Boron | Borax, a mineral (from Arabic bawraq) | 13 | 2 | 10.81 | 2.34 | 2349 | 4200 | 1.026 | 2.04 | 10 |


---
layout: post
title: The Pomodoro time management technique
tags:
- LinkedIn
- Work Practices
status: publish
type: post
published: true
meta:
_edit_last: '451289'
---
I have started to use the Pomodoro technique to try and remove distractions from my work day and focus on what needs to be done. It's very early days yet but it's attractive for a number of reasons:
- It's simple
- The Pomodoro task list needed can be obtained from your GTD next actions list
- It provides a process to handle distractions such as twitter and email
The technique works by allocating work into 30 minutes chunks (called pomodoros) -- 25 minutes to actually work on an specific task and a five minute break to 'rest'. This is designed to stop you getting stale and provides a opportunity to check your email and social media sources without feeling guilty (but you only get 5 mins; and don't forget to re-fill the water bottle and any needed trips to the toilet etc).
The process also recognizes reality by providing tools to handle interruptions and problems. For instance yesterday my clients PC borked so four of my pomodoros did not get done.
So far the process has me feeling much more in control and able to get work done. I'm looking forward to getting familiar with the process and making improvements to my work day.
P.S. I use Pomodairo on my Linux, Windows and iMac to time my pomodoros


Add SVG shapes to canvas:
Add simple shapes to canvas:
Rectangle Circle Triangle Line Polygon
Add images to canvas:
Image 1 (sofa) Image 2 (table) Image 3 (bed)
Add gradient-based shapes to canvas:
Gradient 1 Gradient 2 Gradient 3 Gradient 4
Add arcs and misc to canvas:
Arc(s) 1 Arc(s) 2 Arc(s) 3 Arc(s) 4
Transformed paths
Image
Origin X: Left Center Right
Origin Y: Top Center Bottom


Mascherano linked with switch to David Beckham's Inter Miami
The former Barcelona player could make the move to MLS
by Juan G. ArangoFormer Barcelona player Javier Mascherano is one of the players being targetted by new Major League Soccer franchise Inter Miami, who are owned by David Beckham.
Reports in Argentina this week have suggested that the Argentine midfielder, who left Barça for Hebei Fortune in the Chinese Super League, is now looking for a new challenge with his contract set to expire.
Journalist Cesar Luis Merlo reported that Inter Miami are keen on him and SPORT have been able to confirm that there is an offer on the table.
A source close to Mascherano has said there the American side have made contact and that he is "close" to signing a deal with Beckham's franchise, who will join MLS in 2020.
At 35, there's an element of risk to signing Mascherano, who's best days are behind him. However, he offers experience and character to a relatively young team that is being constructed.
So far, Inter Miami have announced five signings but they have not named a coach. It's been speculated that former Real Madrid coach Santiago Solari is an option.


{% extends "registration/base.html" %} {% block title %}Sign up{% endblock %} {% block content %}
Or, login with GitHub.
{% if form.errors %}Please correct the errors below: {{ form.non_field_errors }}
{% endif %} {% endblock %} {% block content-related %}Fill out the form to the right (all fields are required), and your account will be created; you'll be sent an email with instructions on how to finish your registration.
We'll only use your email to send you signup instructions.
This account will let you log into the ticket tracker, claim tickets, and be exempt from spam filtering.
Your username can only consist of alphanumeric characters and underscores and may be up to 30 characters long.


You enjoy vibrant social events will lots of people.
At parties and similar events you can mostly be found farther away from the action.
When in a group of people you do not know, you have no problem jumping right into their conversation.
You feel more drawn to places with a busting and busy atmosphere than to a more quiet and intimate ones.
When in a public place,you usually stick to quieter and less crowded areas.
You often spend time exploring unrealistic yet intriguing ideas.
You like discussing different views and theories on what the world would look like in the future.
You often drift away into day daydreaming about various ideas or scenarios.
You see yourself as more of a realist than a visionary.
You frequently find yourself wondering how technological advancement could change everyday life.
When it comes to making life-changing choices,you mostly listen to your heart rather than your head.
You often make decisions on a whim.
You often have a hard time understanding other people's feelings.
Your emotions control you more than you control them.
You still honor the commitments you have made even if you have a change of heart.
Your travel plans are more likely to look like a rough list of ideas than a detailed itinerary.
If you have to temporarily put your plans on hold,you make sure it is your top priority to get back on track as soon as possible.
When starting to work on a project, you prefer to make as many decisions upfront as possible.
Even when you have planned a particular daily routine, you usually just end up doing what you feel like at any given moment.
Your personal work style is closer to spontaneous bursts of energy than to organised and consistent efforts.
Your personality type is


Before Change
print("Loading IMDB data...")
(train_texts, train_cats), (dev_texts, dev_cats) = load_data(limit=n_texts)
print("Using %d training examples" % n_texts)
train_docs = [nlp.tokenizer(text) for text in train_texts]
train_gold = [GoldParse(doc, cats=cats) for doc, cats in
zip(train_docs, train_cats)]
train_data = list(zip(train_docs, train_gold))
After Change
print("Loading IMDB data...")
(train_texts, train_cats), (dev_texts, dev_cats) = load_data(limit=n_texts)
print("Using %d training examples" % n_texts)
train_data = list(zip(train_texts,
[{"cats": cats} for cats in train_cats]))
// get names of other pipes to disable them during training
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "textcat"]
with nlp.disable_pipes(*other_pipes): // only train textcat


I was attending a conference in Brazil. The format was the same for each Ayahuasca session. We gathered in a large, airy room that is screened on all sides and built around a pole in the center. We would share our intentions for each of the sessions and each tie a knot in a length of wool, when we were all done L (one of the facilitators) would knot the ends, symbolizing our circle, and giving us an anchor to come back to. In the sharing the day after each session, we would each untie a knot as we shared whatever we wished to about our experiences. The room was darkened and we would all lie on our own mattresses while it hit. There was music on the way up, traditional chants, rain sounds, soothing music, and L walked around the room chanting and shaking a gourd rattle.
Session 4
Radiance, Singing through panic, Nephandi lovers come home to roost, loving Jonathan
Dosage: 80ml.
Physical effects: The usual diarrhea. No vomiting. Physical lethargy during the early part, followed by energy and just plain randiness.
I started the night off as a radiant child, the eternal child, the Fool card from the tarot deck, blithely dancing off the cliff. Jonathan smiled at me and said 'hey, cosmic child.'
I panicked as it hit, it always begins for me with this tight feeling in the roof of my mouth, akin to the feeling I would get when very sick, delirious with fever as a child.
I sang through the panic, I'm not sure what, it was apparently pleasing to those near me as Gary told me afterwards that I have a magical voice. I took some excellent self-portrait photos on the way up, probably the best photographs I have ever taken of myself.
I saw my comrades' souls as glowing, dancing balls of light, some rainbow, some specific hues, all dancing. This was probably fueled by Jonathan dancing with blue and gold keychain lights. Beautiful. I got flashes of images as I climbed, I felt the Nephandi I invented in the Twilight War, I saw the contamination in the people of Boston, the energy chain that linked the victims to the Nephandi. I realized it would all make a good story and that I should write it up, perhaps with just enough changed to not get sued by White Wolf, or check with White Wolf and have them pay me for the story.
Kyle was there, speaking to me and somehow I was Leigh. I felt Kyle touch my mind lightly and he whispered 'I am here. I have always been here. I waited until you came into your power because you were not of much use to me until now. But now, you are mine, as you have always been.' With his touch came intense sexual longing, my body and soul was on fire, wanting him. And I felt my second soul, the dragon soul, awaken.
I saw Rachel, saw her call the fire spirits, saw the explosion light all the worlds.
Somewhere in all of this I cast off my chemise and vest, feeling like I was shedding a snakeskin. I felt so much more comfortable and free with my upper body naked, so I remained so for most of the night.
I came out of this delirium to discover that darkness had fallen while I dreamed. I got up and went outside, finding Jonathan there. We cuddled, kissed and talked. Our clothes stayed on, but we were very close to making love under the stars. It was a beautiful night and I was very much in love with him.
I saw B and we spoke a little. Jonathan tried to play relationship counsellor but B was having none of it. He did manage to reassure me that he is still in love with me; somehow I was finally able to feel it and believe it in a way I hadn't been able to the previous times. I asked him if there is anything I am doing here or could do here that would make him love me less and he said that there is not. In some way I both accepted him deeper into my heart and let him go all at the same time.
I was very catlike through the sharing, cuddling with Jonathan. Alex scritched me behind the ears.
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.


Smoke Signals (1998, PG-13)
Directed by Chris Eyre
Written by Sherman Alexie (based on stories from his book The Lone Ranger and Tanto Fistfight in Heaven)
Starring Adam Beach, Evan Adams, Irene Bedard, Gary Farmer, Cody Lightning, and Simon Baker
As Reviewed by James Brundage (MovieKritic2000)
To those who think the world of watching movies as a job all fun and games, remember that it is a chore as well. You are forced to sit through numerous bad movies, forced to lose faith slowly and surely in the film industry until it is almost gone.
Then, as hope has dwindled to an almost non-existent point, you sit back and watch a film like Smoke Signals. Smoke Signals is one of those rare movies that you never hear a bad word about. It is one of those films that comes out of nowhere, has no big names or bad lines, and is a completely original story… in short, the main things most movies lack.

Set on the C'Ordelene Indiana Reservation in Idaho, Smoke Signals concerns Victor Joseph (Adam Beach as an adult, Cody Lightning as a child) and Thomas Builds-the-Fire (Evan Adams as an adult, Simon Baker as a child), both, according to Sherman Alexie, "children born of fire." By this it is meant that the two of them were both almost killed in the same fire that took Thomas' parents 22 years ago on the Fourth of July. Arnold Joseph (Gary Farmer) saved Thomas by catching him in his arms when he was thrown out of the window by his parents in order to save him from the holocaust.
Arnold Joseph, haunted by his own guilt for his abusive treatment of his family and a secret that tears him apart inside, left for Phoenix when Thomas and Victor were only 12, and, ten years later, the two of them must go retrieve his ashes from his next door neighbor Suzy Song (Irene Bedard).
The story sounds highly simplistic, and it is. It also serves to be a perfect method in which to examine the troubled relationships between fathers and sons, and seeks to find a definition of a good man.
As much as I talk of myself in my reviews, writing this it strikes me how very little I tell you in them. I opt not to speak of my own relationships with others, nor my own personal demons and hauntings. Faced with Smoke Signals, I would love to recommend the film more to you but cannot do so without entering into how the film is made personal for me.
That feeling that the film is personal is universal with Smoke Signals, which means different things to different people but is always a powerful and personal story to everyone. The film is strictly word-of-mouth, recommended by one friend to another, and there is a reason for that. Smoke Signals is so intensely personal that it is difficult to get into why it is such a perfect movie without drifting into one's own past… something which most people will only do with people they trust.
What can be told without getting too personal is that, whatever walls you may have built up over the course of your life, Smoke Signals contains with it the raw power to break them with a thought. It is a film that you will enjoy, whether or not you set out to enjoy it.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Filesystem operations, e.g., moving the database environment to a different machine or file creation, deletion or renaming, cannot be transaction protected. For this reason, files cannot be re-created, deleted or renamed as part of the recovery process.
If recovery cannot find a database file referenced in the log, it will output a warning message that it was unable to locate a file it expected to find. This message is only a warning, as the file may have subsequently been deleted as part of normal database operations before the failure occurred, and so cannot be known to be a problem.
Note that committed transactions that involved these missing files are rolled forward, even though the files were not found. If the files were not intentionally deleted (e.g., they were created after the last database snapshot, but were lost during a failure), they must be manually created (using db_open), and then recovery must be redone.
Generally, it is simplest to perform filesystem operations at the same time as making a snapshot of the database. To perform filesystem operations:
To cleanly shutdown database operations, all applications accessing the database environment must be shutdown and a transaction checkpoint must be taken. If the applications are not implemented such that they can be shutdown gracefully (i.e., closing all references to the database environment), recovery must be performed after all applications have been killed to ensure that the underlying databases are consistent on disk.
Future versions of Berkeley DB are expected to remove the restriction that database files must be manually created before recovery is performed.


For Question 7, I'm not very familiar with the concept of efficiency and the number of times to call a method. Random methods' average calls are not very facile.
A lot of other problems are answered wrong because I was not very careful when reading the questions. Next time I should read them more carefully and slowly.
Speaking of reading slowly, I should increase the speed of answering questions, since I left blank in last two questions, of which I lost lots of marks. Why? Because I thought there were only 13 questions in the paper, and I stopped writing as soon as I reached Question 13. I should be more careful next time. I should go through the whole paper first then answer the question.
P8 (E) I didn't consider the question comprehensively, so I lost marks on it. I should slow down at the questions with big marks
Discussions


A colorful blob gane with two testing scenarios.
This application allows user to create tasks, sort, load, and save them in local storage.
A game where users can play "rock, paper, scissors" against the computer while the score is recorded.
Randomly scramble and unscramble the Wellesley logo, using the W A S D keys.


A protected anchorage in what was once a secluded fishing village, but is now more aimed at tourists. It can get noisy in the evenings and crowded with holidaymakers at weekends.
Position 22°45'S, 41°53'W.
Moorings
There are moorings available from ICAB which are registered with the Maritime Authorities and are checked twice yearly by professional divers. However in 2011 a cruising yacht anchored downwind of the YC moorings did suffer a collision with a large boat that was using one of these moorings that dragged. Therefore it is advisable to anchor elsewhere.
Anchorages
There are various anchorages along the shores of the town. The best anchorage is oposite the last house in the village, in the SW corner of the bay. In northerly winds, there is adequate protection to the north of the yacht club, on the southern side of the peninsular, but you are more exposed to the constant SE swell. There is more room to anchor in the other near-by bays, but they are less protected from the east.
Beware of the many, insufficiently marked, local fishing devices.
Last updated January 2012.


Whatever you want to do, however skilled you are, Having a good idea is essential before you start anything, How yo get Ideas? Good ideas can be formed only when you know what the world is doing, to know what the world is doing, you should have a platform where experts share ideans openly. ProjectHub provides this facility. Start exploring today!
ExploreProjects uploaded on ProjectHub.com are completely open source and you can just steal the code without any patents or licensing issues, Idea of Open Source is the major key to large scale innovation, for example if Vint Cerf and Bob Khan would't have Open sourced their research on TCP/IP then there would no fast and robust internet as it is today, the same is with Linux, if Linux was a propritery product, then there would be no android phones or Cloud servers. If you are in ProjectHub you are the part of Open Source community.
We use GitHub as a repository to store source code, it has many advantages, you can redirect users to gitHub site directly, you upload code only once and link it everywhere there is no need of uploading code again and again.
ProjectHub provides you with a facility to provide feedbacks, In many cases Feedbacks may contain useful information abou the faluts in the code or product you are developing.You can provide some meaningful feedbacks or you can learn from others feedbacks, Feedbacks are available on profile management page.
When you explore ProjectHub, we make sure top developers are always appreciated, we do so by putting them in the trending developers section, this helps them to expand their reach and get more likes for their work. From user's point of you they can get to know what top developers are doing.
Trending developersIn the same way like we appreciate developers we appreciate top projects from each domain, maybe it is from Machine Learning, Java or Python. We put them in the trending projects section as a token of appreciation and Users can get to know the latest trends in development.
Trending projectsAre you a programming wizad? Well, It does not mean you don't know how to write! We provide you an oppurtunity to write articles, it can be about tech, general knowledge, Universal basic Income, Science, Machine learning concerns , your announcements etc.
If you have time, It's better to go through better articles on ProjectHub.com, It will help you to get knowledge about various technologies and products available.
View Articles


---
layout: post
mathjax: true
comments: true
date: 2012-12-15
title: [Project Euler] #21 Solution
excerpt: |
This page presents a Ruby solution to Project Euler Problem 21.
tags:
- project euler problem 21 solution
- project euler problem 21 answer
- project euler problem 21 ruby
---
Overview
Question
Let d(n) be defined as the sum of proper divisors of n (numbers less than n which divide evenly into n).
If d(a) = b and d(b) = a, where a b, then a and b are an amicable pair and each of a and b are called amicable numbers.
For example, the proper divisors of 220 are 1, 2, 4, 5, 10, 11, 20, 22, 44, 55 and 110; therefore d(220) = 284. The proper divisors of 284 are 1, 2, 4, 71 and 142; so d(284) = 220.
Evaluate the sum of all the amicable numbers under 10000.
click for answer
31626
Solutions
Ruby
{% highlight ruby %} amicableSums.rb https://raw.github.com/addamh/euler/master/021/amicableSums.rb download
#!/usr/bin/env ruby
final_sum=0
2.upto(9999) do |a|
sum1=0
1.upto(a/2) do |b|
if a%b == 0
sum1+=b
end
end
sum2=0
if sum1


{{ site.user_description }}
"Breaking out of established patterns to look at things in a different way."
-A wild mind with a disciplined eye


|
| def | __init__ (self, name) |
| |
| def | __repr__ (self) |
| |
| def | __str__ (self) |
| |
| def | cleanup (self) |
| |
| def | defaultEnter (self, *args) |
| |
| def | defaultExit (self) |
| |
| def | defaultFilter (self, request, args) |
| |
| def | demand (self, request, *args) |
| |
| def | filterOff (self, request, args) |
| |
| def | forceTransition (self, request, *args) |
| |
| def | getCurrentFilter (self) |
| |
| def | getCurrentOrNextState (self) |
| |
| def | getCurrentStateOrTransition (self) |
| |
| def | getStateChangeEvent (self) |
| |
| def | isInTransition (self) |
| |
| def | request (self, request, *args) |
| |
| def | requestNext (self, *args) |
| |
| def | requestPrev (self, *args) |
| |
| def | setBroadcastStateChanges (self, doBroadcast) |
| |
| def | setStateArray (self, stateArray) |
| |
| def | __init__ (self) |
| |
| def | accept (self, event, method, extraArgs=[]) |
| |
| def | acceptOnce (self, event, method, extraArgs=[]) |
| |
| def | addTask (self, *args, **kwargs) |
| |
| def | detectLeaks (self) |
| |
| def | doMethodLater (self, *args, **kwargs) |
| |
| def | getAllAccepting (self) |
| |
| def | ignore (self, event) |
| |
| def | ignoreAll (self) |
| |
| def | isAccepting (self, event) |
| |
| def | isIgnoring (self, event) |
| |
| def | removeAllTasks (self) |
| |
| def | removeTask (self, taskOrName) |
| |
A Finite State Machine. This is intended to be the base class
of any number of specific machines, which consist of a collection
of states and transitions, and rules to switch between states
according to arbitrary input data.
The states of an FSM are defined implicitly. Each state is
identified by a string, which by convention begins with a capital
letter. (Also by convention, strings passed to request that are
not state names should begin with a lowercase letter.)
To define specialized behavior when entering or exiting a
particular state, define a method named enterState() and/or
exitState(), where "State" is the name of the state, e.g.::
def enterRed(self):
... do stuff ...
def exitRed(self):
... cleanup stuff ...
def enterYellow(self):
... do stuff ...
def exitYellow(self):
... cleanup stuff ...
def enterGreen(self):
... do stuff ...
def exitGreen(self):
... cleanup stuff ...
Both functions can access the previous state name as
self.oldState, and the new state name we are transitioning to as
self.newState. (Of course, in enterRed(), self.newState will
always be "Red", and the in exitRed(), self.oldState will always
be "Red".)
Both functions are optional. If either function is omitted, the
state is still defined, but nothing is done when transitioning
into (or out of) the state.
Additionally, you may define a filterState() function for each
state. The purpose of this function is to decide what state to
transition to next, if any, on receipt of a particular input. The
input is always a string and a tuple of optional parameters (which
is often empty), and the return value should either be None to do
nothing, or the name of the state to transition into. For
example::
def filterRed(self, request, args):
if request in ['Green']:
return (request,) + args
return None
def filterYellow(self, request, args):
if request in ['Red']:
return (request,) + args
return None
def filterGreen(self, request, args):
if request in ['Yellow']:
return (request,) + args
return None
As above, the filterState() functions are optional. If any is
omitted, the defaultFilter() method is called instead. A standard
implementation of defaultFilter() is provided, which may be
overridden in a derived class to change the behavior on an
unexpected transition.
If self.defaultTransitions is left unassigned, then the standard
implementation of defaultFilter() will return None for any
lowercase transition name and allow any uppercase transition name
(this assumes that an uppercase name is a request to go directly
to a particular state by name).
self.state may be queried at any time other than during the
handling of the enter() and exit() functions. During these
functions, self.state contains the value None (you are not really
in any state during the transition). However, during a transition
you *can* query the outgoing and incoming states, respectively,
via self.oldState and self.newState. At other times, self.state
contains the name of the current state.
Initially, the FSM is in state 'Off'. It does not call enterOff()
at construction time; it is simply in Off already by convention.
If you need to call code in enterOff() to initialize your FSM
properly, call it explicitly in the constructor. Similarly, when
`cleanup()` is called or the FSM is destructed, the FSM transitions
back to 'Off' by convention. (It does call enterOff() at this
point, but does not call exitOff().)
To implement nested hierarchical FSM's, simply create a nested FSM
and store it on the class within the appropriate enterState()
function, and clean it up within the corresponding exitState()
function.
There is a way to define specialized transition behavior between
two particular states. This is done by defining a from<X>To<Y>()
function, where X is the old state and Y is the new state. If this
is defined, it will be run in place of the exit<X> and enter<Y>
functions, so if you want that behavior, you'll have to call them
specifically. Otherwise, you can completely replace that transition's
behavior.
See the code in SampleFSM.py for further examples.


Age UK shop volunteers flies the flag for veterans with Remembrance tribute
Staff and volunteers at a Black Country Age UK shop dressed up in their 1940s best for Remembrance Day.
by Dayna FarringtonThe Blackheath Age UK store was decorated with old photos and posters between November 11 and 14.
And staff also dressed up in their 1940s finest – including a nurse, land girl and cadet.
Shop manager Claire Toy said: "We always do a Remembrance Day window display and this year our volunteers decided to make it special. We have printed old photos, dressed in 1940s clothes, played 1940s music.
"We had a land girl, cadet, nurse and I dressed up in dresses with red lipstick.
"We have had an amazing week and listened to amazing stories from shoppers of days gone by. People coming in to the shop have been reminiscing.
"It has been really rewarding for us. We get a lot of old people coming into the shop showing us photos of their family members.
"We are definitely going to do it again next year – and we are hoping to get more local photos."


---
layout: portal
category: reject
tags: reject
id: 68
imgurl: https://lh4.ggpht.com/m6szA9zY3aAjgeHHO6MfDWN_Urv1_la1CT-czr14W__ISVvmJc2qRWa41IDcsI9abKefNim9syPcqZsyBys
portalname: パンダ
sentdate: Mon Mar 30 22:08:31 JST 2015
title: 68 - パンダ
---
From: Ingress Operations<EMAIL_ADDRESS>日付: 2015年3月26日木曜日
件名: Ingress Portal Rejected: パンダ
Thank you for your Portal submission. However, this Portal candidate does not meet the criteria required for approval.
Please refer to New Portal Submission criteria at our Help Center for further information.


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Add your email address.
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Enter your password.
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Confirm your passowrd.
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RANDOM Function
The RANDOM function generates a random value between 0.0 and 1.0.
Syntax
RANDOM()
Return Type
RANDOM returns a DOUBLE PRECISION number.
Usage Notes
Call RANDOM after setting a seed value with the SET command to cause RANDOM to generate numbers in a predictable sequence.
Examples
Compute a random value between 0 and 99. If the random number is 0 to 1, this query produces a random number from 0 to 100:
select cast (random() * 100 as int); int4 ------ 24 (1 row)
This example uses the SET command to set a SEED value so that RANDOM generates a predictable sequence of numbers.
First, return three RANDOM integers without setting the SEED value first:
select cast (random() * 100 as int); int4 ------ 6 (1 row) select cast (random() * 100 as int); int4 ------ 68 (1 row) select cast (random() * 100 as int); int4 ------ 56 (1 row)
Now, set the SEED value to .25
, and return three more RANDOM numbers:
set seed to .25; select cast (random() * 100 as int); int4 ------ 21 (1 row) select cast (random() * 100 as int); int4 ------ 79 (1 row) select cast (random() * 100 as int); int4 ------ 12 (1 row)
Finally, reset the SEED value to .25
, and verify that RANDOM returns
the same results as the previous three calls:
set seed to .25; select cast (random() * 100 as int); int4 ------ 21 (1 row) select cast (random() * 100 as int); int4 ------ 79 (1 row) select cast (random() * 100 as int); int4 ------ 12 (1 row)


--- type: formControls title: Dropdown Modifiers usage: Modifiers for the Dropdown control { mod-menu, mod-right } . stylesheet: '/scss/controls/dropdown.scss' ---


I/O Ports
The QuickUSB Diagnostics for the Cypress EZ USB-FX2 gives you the power to directly control 3 8-bit parallel ports from the comfort of your PC without writing any firmware.
This is accomplished using the following QuickUSB Library functions:
QuickUsbWritePortDir - Specifies the direction (input or output) of each pin of a port
QuickUsbWritePort - Specifies the value of an output pin.
QuickUsbReadPort - Reads the current value of all the pins of a port. Outputs return the output value and inputs return the value on the pin.
The I/O port tab is shown below:
Port A - P2, Pins 12-19
Pin Direction
Pin Value
Read
Port C - P3, Pins 12-19
Pin Direction
Pin Value
Read
Port E - P6, Pins 12-19
Pin Direction
Pin Value
Read


What time is displayed on the clock?
Choose time
Half past
Quarter past
Quarter to
O'clock
1
2
3
4
5
6
7
8
9
10
11
12
Answered questions: 0
Correct answers: 0%


Genetics and Ancestory
by Beata Mulvaney
Discovering where you come from and the stories of your ancestors is like being your own personal detective.
In many families, there seems to be at least one person who is interested in genealogy. Advances in the field of genomics have made it possible to use a person’s DNA to find out where their ancestors may have come from.
Once you know more about your ancestors, you could research to find out if you are able to confirm your second nationality and perhaps to apply for second passport for yourself and your family. This could be helpful if things change in the world of politics, Brexit etc. Who knows what will happen in years to come?
A number of companies these days offer genetics testing, which determines ancestry, by comparing your genes to known reference populations, to see which ones they resemble most. “Mitochondrial DNA” (the special piece of DNA that’s handed down from mother to child) traces back to a common female ancestor in certain regions.
As genome research continues at a greater pace than ever before, sometime in the next decade the cost of having your whole genome sequenced - all three billion letters of the code - will become affordable. When that happens, it will change genetic genealogy forever.
“What you’ll be able to do is look at an individual’s genome and say, all right, they have this mutation, which arose in a particular village in the south of France, for example,” says Harvard genetecist Joe Pickrell.
“You’d be able to say with nearly 100% certainty that you have some ancestor who came from that particular village.”
If you are looking to find out who your ancestors were, if you are interested in discovering your family history, the evolving world of genomics can give you a greater sense of identity and your place in the world.
If you love travelling, discovering the world you probably already know, there is so much out there to see and do. In this day and age, most of us no longer belong just to one country or region.
Beata Mulvaney
Specialist Interpreter
- Author: Beata Mulvaney
- Date: January 2019
- Category: Blog


BigW is an Australian Kmart competitor. For this role, Core was contracted in the assistance to help develop the recommendation engine for BigW’s email campaign. Core investigated a wide variety of recommendation engine technologies whose results ultimately decided upon the Machine Learning technology to be deployed by WooliesX & BigW.
During the Covid-19 Crisis, Core created a simple question answering tool in order to help individuals and SMEs cut through the noise to help find what private & government financial stimulus aide they were eligible for.
Core Intelligence is the principal partner to Blink Securetech where Core has assisted in the conception, development & maintenance of the Blink Accounting platform. Blink Accounting is a machine learning powered webapp that provides accountants a high level overview of their clients financial position via various metrics such as Cash Flow. Via ingesting data into Core’s machine learning engines, Blink Accounting is able to serve up various recommendations, such as future cash flow forecasts, to Accountants on how they can improve their client’s overall net position.
Blink Cyber is the other arm of Blink Securetech purely focussing on Cyber Risk & Mitigation. For the Blink Cyber webapp, Core Intelligence designed from first principles a Machine Learning powered recommendation engine. Users answer a questionnaire, whose responses are pushed into the recommendation engine to provide the right tailor made personalised insurance policy for the client.
Qualie is a company that collects both Qualitative - a users opinion - and Quantitative - facts such as numbers survey responses. For this engagement, Core designed a state of the art reinforcement learning algorithm to control how videos were served to Qualie’s users in order to collect data.
Via the use of Core Intelligence’s proprietary question and answer engine, Core was engaged to develop a webapp to display questions & collect responses from Citizens for a new green field residential build.
Core Intelligence was engaged by a major US pet insurance company to deliver a minimum viable product web app design for their product - Petin. The webapp consisted of a questionnaire, collecting user’s responses, displaying information via a dashboard, then use machine learning to create, entirely from scratch, an insurance policy with the right clauses tailored specifically to the user’s answers from the questionnaire.


Serializers and parsers for the Xebu format. This package contains everything needed to use the Xebu format for XML data in programs.
The serializer and parser are implementations of the XmlPull APIs {@link org.xmlpull.v1.XmlSerializer} and {@link org.xmlpull.v1.XmlPullParser}, respectively, so these classes can be used as drop-in replacements in existing applications.


{% extends "base.html" %} {% block content %} This is a Progressive web app with Flask If your browser supports pwa (like chrome) You should see an install button or "Install app" in chrome mobile. Flask-PWA {% endblock content %}


Library: XML
Package: SAX
Header: Poco/SAX/Attributes.h
Description
Interface for a list of XML attributes. This interface allows access to a list of attributes in three different ways:
- by attribute index;
- by Namespace-qualified name; or
- by qualified (prefixed) name.
The list will not contain attributes that were declared #IMPLIED but not specified in the start tag. It will also not contain attributes used as Namespace declarations (xmlns*) unless the http://xml.org/sax/features/namespace-prefixes feature is set to true (it is false by default).
If the namespace-prefixes feature (see above) is false, access by qualified name may not be available; if the http://xml.org/sax/features/namespaces feature is false, access by Namespace-qualified names may not be available. This interface replaces the now-deprecated SAX1 AttributeList interface, which does not contain Namespace support. In addition to Namespace support, it adds the getIndex methods (below). The order of attributes in the list is unspecified, and will vary from implementation to implementation.
Inheritance
Known Derived Classes: AttributesImpl
Member Summary
Member Functions: getIndex, getLength, getLocalName, getQName, getType, getURI, getValue
Destructor
~Attributes
virtual ~Attributes();
Member Functions
getIndex
virtual int getIndex(
const XMLString & name
) const = 0;
Look up the index of an attribute by a qualified name.
getIndex
virtual int getIndex(
const XMLString & namespaceURI,
const XMLString & localName
) const = 0;
Look up the index of an attribute by a namspace name.
getLength
virtual int getLength() const = 0;
Return the number of attributes in the list.
Once you know the number of attributes, you can iterate through the list.
getLocalName
virtual const XMLString & getLocalName(
int i
) const = 0;
Look up a local attribute name by index.
getQName
virtual const XMLString & getQName(
int i
) const = 0;
Look up a qualified attribute name by index.
getType
virtual const XMLString & getType(
int i
) const = 0;
Look up an attribute type by index.
The attribute type is one of the strings "CDATA", "ID", "IDREF", "IDREFS", "NMTOKEN", "NMTOKENS", "ENTITY", "ENTITIES", or "NOTATION" (always in upper case).
If the parser has not read a declaration for the attribute, or if the parser does not report attribute types, then it must return the value "CDATA" as stated in the XML 1.0 Recommendation (clause 3.3.3, "Attribute-Value Normalization").
For an enumerated attribute that is not a notation, the parser will report the type as "NMTOKEN".
getType
virtual const XMLString & getType(
const XMLString & qname
) const = 0;
Look up an attribute type by a qualified name.
See getType(int) for a description of the possible types.
getType
virtual const XMLString & getType(
const XMLString & namespaceURI,
const XMLString & localName
) const = 0;
Look up an attribute type by a namespace name.
See getType(int) for a description of the possible types.
getURI
virtual const XMLString & getURI(
int i
) const = 0;
Look up a namespace URI by index.
getValue
virtual const XMLString & getValue(
int i
) const = 0;
Look up an attribute value by index.
If the attribute value is a list of tokens (IDREFS, ENTITIES, or NMTOKENS), the tokens will be concatenated into a single string with each token separated by a single space.
getValue
virtual const XMLString & getValue(
const XMLString & qname
) const = 0;
Look up an attribute value by a qualified name.
See getValue(int) for a description of the possible values.
getValue
virtual const XMLString & getValue(
const XMLString & uri,
const XMLString & localName
) const = 0;
Look up an attribute value by a namespace name.
See getValue(int) for a description of the possible values.


王钰迪
Computational Optimization Lab
Stanford University Palo Alto, Jul,2016-Present
Full time Research Assistant, Advisor: Yinyu YE, K. T. Li Chair Professor of Engineering
National Bureau of Economic Research
Harvard University Boston, Feb,2015-March,2015
Full time Research Assistant, Advisor: Richard B Freeman, the Herbert Ascherman Chair in Economics at Harvard University
Tencent Holding Ltd
Shenzhen, Jul,2014-Sep,2014
Summer Intern, Product Manager, Coporate Development Group
E-Commerce Lab
Tianjin University Tianjin, May,2015-August,2016
Full time Research Assistant, Advisor: Xi ZHANG, professor of Tianjin University
Tianjin University Tianjin, Sep,2013-Present
University of California, Los Angeles Los Angeles, Mar,2016-Jun,2016
Languages: Chinese (native), Cantonese (native), English (fluent)
Skills: Skilled at C, C++, C#, Lisp, MATLAB, R, ASP.NET, Markdown, Latex, Justinmind;
Proficient with Python, Java, Photoshop


var BookTitles = []string{"beowulf.txt", "sherlock_holmes.txt", "tale_of_two_cities.txt", "ulysses.txt"}
func ConvertBooksToDs3Objects(books map[string]networking.ReaderWithSizeDecorator) ([]models.Ds3PutObject, error)
func ConvertObjectsIntoObjectNameList(objects []models.Contents) []string
func DeleteBucket(client *ds3.Client, bucketName string) error
Deletes the specified bucket and returns an error if one occurs
func DeleteBucketContents(client *ds3.Client, bucketName string)
func DeleteBucketLogError(t *testing.T, client *ds3.Client, bucketName string) error
Deletes the specified bucket. If an error occurs, it is logged, and the calling test is marked as failed.
func DeleteDataPolicyLogError(t *testing.T, client *ds3.Client, dataPolicyId string) error
Deletes the specified data policy. If an error occurs, it is logged, and the calling test is marked as failed, but continues running.
func DeleteObject(client *ds3.Client, bucketName string, objectName string) error
Deletes the specified object. Returns an error if not successful.
func DeleteObjectLogError(t *testing.T, client *ds3.Client, bucketName string, objectName string) error
Deletes the specified object. If an error occurs, it is logged, and the calling test is marked as failed.
func GetBucket(client *ds3.Client, bucketName string) (*models.GetBucketResponse, error)
Retrieves the specified bucket. Returns an error if unsuccessful.
func GetBucketLogError(t *testing.T, client *ds3.Client, bucketName string) (*models.GetBucketResponse, error)
Retrieves the specified bucket. If an error occurs, it is logged, and the calling test is marked as failed.
func GetDataPolicyLogError(t *testing.T, client *ds3.Client, dataPolicyId string) (*models.GetDataPolicySpectraS3Response, error)
Retrieves the specified data policy. If an error occurs, it is logged, and the calling test is marked as failed, but continues running
func GetObject(client *ds3.Client, bucketName string, objectName string) (*models.GetObjectResponse, error)
Retrieves the specified object. Returns an error if not successful.
func GetObjectLogError(t *testing.T, client *ds3.Client, bucketName string, objectName string) (*models.GetObjectResponse, error)
Retrieves the specified object. If an error occurs, it is logged, and the calling test is marked as failed.
func GetResourceBookAsStream(book string) (*networking.ReaderWithSizeDecorator, error)
func GetResourceBooks() (map[string]networking.ReaderWithSizeDecorator, error)
func GetUserByNameLogError(t *testing.T, client *ds3.Client, userName string) (*models.SpectraUser, error)
Retrieves the specified user by name. Expects there to be one user with the specified name. If there is not exactly 1 user with the specified name, it is treated as an error. If an error occurs, it is logged, and the calling test is marked as failed, but continues running.
func LoadBook(book string) ([]byte, error)
Loads the specified book. Assumes that the book is located in the /resources/books/ folder.
func LoadBookLogError(t *testing.T, book string) ([]byte, error)
Loads the specified book. If an error occurs, it is logged, and the calling test is marked as failed.
func PutBucket(client *ds3.Client, bucketName string) error
Puts the specified bucket. Returns an error if not successful.
func PutBucketLogError(t *testing.T, client *ds3.Client, bucketName string) error
Puts the specified bucket. If an error occurs, it is logged, and the calling test is marked as failed.
func PutDataPolicyLogError(t *testing.T, client *ds3.Client, dataPolicyName string) (*models.PutDataPolicySpectraS3Response, error)
Creates the specified data policy. If an error occurs, it is logged, and the calling test is marked as failed, but continues running.
func PutObject(client *ds3.Client, bucketName string, objectName string, data []byte) error
Puts the specified object. Returns an error if not successful.
func PutObjectLogError(t *testing.T, client *ds3.Client, bucketName string, objectName string, data []byte) error
Puts the specified object. If an error occurs, it is logged, and the calling test is marked as failed.
func PutTestBooks(client *ds3.Client, bucketName string) error
Puts the test books onto the BP
func SetupTestEnv(testBucket string, userName string, envTestNameSpace string) (*ds3.Client, *TestIds, error)
Sets up the test environment by 1) creating a client from environment variables and 2) creating a test bucket 3) creating new data policy 4) setting default user to use new data policy 5) crating a pool partition 6) creating a storage domain 7) creating a storage domain member 8) creating a data persistence rule
func TeardownTestEnv(client *ds3.Client, ids *TestIds, testBucket string)
Tears down the test environment by deleting all contents in the test bucket and deleting all items created in SetupTestEnv
func VerifyBookContent(t *testing.T, bookName string, content io.ReadCloser)
type TestIds struct { DataPersistenceRuleId *string DataPolicyId *string OriginalDataPolicyId *string PoolPartitionId *string StorageDomainId *string StorageDomainMemberId *string UserId *string }
Used to keep track of all IDs for items created in SetupTestEnv, and used in TeardownTestEnv. Items that have not been initialized are represented with nil, denoting no teardown is required for that element.


No one has clicked me yet :(
----------------------
You can even change ths color of the page if you want Click me!


Meet The Team
Early Career Advisory Board of
ACS Sustainable Chemistry & Engineering
Acta Physico-Chimica Sinica
Journal of Electrochemistry
Frontiers in Energy
Jiajun Zhao
2019.09-now Master studentoxygen reduction and IR spectroscopy
Ke Ye
2020.05-now Ph.D candidateCO2 reduction, spectroelectrochemistry
Shusheng Wan
2020.07-now Master studentwater splitting, Raman spectroscopy
Xiaojun Wang
2020.07-now Master studentwater splitting and electrolyzer
Jun Liu
2020.09-now Master studentCO2 reduction related catalysts & reactor
Guiru Zhang
2021.07-now Research associateCO2 reduction related catalysts & reactor
Join Us
Send Email tokunjiang@sjtu.edu.cn


Im Manaswitha
In a nutshell , I would like to rewind what I have done in 2018
To start with , I have taken 2 courses at NEU during the spring semester
Early May, I started my first job as a data science intern at Channing division of network medicine, Brigham and women's Hospital
It has been a great experience working with an amazing team of physicians, bioinformaticians and researchers on COPD omics data
Late December, I have been to an adventurous vacation with my family to South east Asia.
Well I would say that I had experienced manythings for the first time ever in 2018


Chaton Web Technology
Reaction Tester
Click on the boxes and circles as quickly as you can!
Your time:
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| FutureCondition {future} | R Documentation |
While orchestrating (creating, launching, querying, collection) futures, unexpected run-time errors (and other types of conditions) may occur. Such conditions are coerced to a corresponding FutureCondition class to help distinguish them from conditions that occur due to the evaluation of the future.
FutureCondition(message, call = NULL, future = NULL) FutureMessage(message, call = NULL, future = NULL) FutureWarning(message, call = NULL, future = NULL) FutureError(message, call = NULL, future = NULL) RngFutureCondition(message = NULL, call = NULL, future) RngFutureWarning(...) RngFutureError(...) UnexpectedFutureResultError(future, hint = NULL)
message |
A message condition. |
call |
The call stack that led up to the condition. |
future |
The Future involved. |
hint |
(optional) A string with a suggestion on what might be wrong. |
An object of class FutureCondition which inherits from class condition and FutureMessage, FutureWarning, and FutureError all inherits from FutureCondition. Moreover, a FutureError inherits from error, a FutureWarning from warning, and a FutureMessage from message.


This file is used by Testling-CI to run the client- side Paws tests in each browser I'm interested in testing against, automatically. It outputs the information in Test-Anything-Protocol (or ‘TAP’) format to the browser's console.
You'll see a much prettier version of our client-side tests if you run cake test:client in the project's directory.
:)


---
layout: default
title: Projects
bodyclass: listing
---
Here you can see the projects I developed or contributed to.
See also my references for more work by me.
{% assign projects = site.categories.projects | sort:"order" %}
{% for post in projects %}
{% include project.html %}
{% endfor %}


{% extends "base.html" %} {% block content %}
Atlas is a mobile focused web application designed to help users manage their spare time, specifically when trying to learn skills.
After minimal set up, Atlas takes out the effort and simply provides you with a todo list of tasks that fit into any time frame, every day.
The backend is a Django REST API, and the frontend is generated with Django's templating system. While it may seem redundant to build the application in this way, it was a conscious decision. The reason being that this is a proof of concept, built in a way that is simultaneously forward thinking and the quickest way to technologically solve the problem outlined in the What section. As I use/test the application, I will discover what works and what doesn't, and use this information to refine the app and build out a more feature rich front end (using a fit for purpose front end framework) and a native mobile app. Using an API allows for this extensibility, and using Django's own templating system ensured the quickest and simplest solution.
The code for this application can be viewed on github.
App stores in every ecosystem are saturated with ToDo apps. Despite this, I was unable to find one that scratched the itch I had. During my time working at a digital agency, my work was streamlined by my project manager, which freed me from the need to organise my time allowing me to focus on my work. I had a strong desire for such a workflow in my spare time, as I had many hobbies; keeping track of what I should be doing in each one to further my competence became tedious. And so, Atlas was born.


SMART CITY FEATURES
Some typical strategy of comprehensive development in Smart Cities are described below.
- The strategic components of area-based development in the Smart Cities Mission are city improvement (retrofitting), city renewal (redevelopment)
and city extension (greenfield development) plus a Pan-city initiative in which Smart Solutions are applied covering larger parts of the city.
Below are given the deions of the three models of Area-based smart city development:
- Retrofitting will introduce planning in an existing built-up area to achieve smart city objectives, along with other objectives, to make the
existing area more efficient and liveable. In retrofitting, an area consisting of more than 500 acres will be identified by the city in
consultation with citizens. Depending on the existing level of infrastructure services in the identified area and the vision of the residents,
the cities will prepare a strategy to become smart. Since existing structures are largely to remain intact in this model, it is expected that
more intensive infrastructure service levels and a large number of smart applications will be packed into the retrofitted smart city. This
strategy may also be completed in a shorter time frame, leading to its replication in another part of the city.
- Redevelopment will effect a replacement of the existing built-up environment and enable co-creation of a new layout with enhanced infrastructure
using mixed land use and increased density. Redevelopment envisages an area of more than 50 acres, identified by Urban Local Bodies (ULBs) in
consultation with citizens. For instance, a new layout plan of the identified area will be prepared with mixed land-use, higher FSI and high
ground coverage. Two examples of the redevelopment model are the Saifee Burhani Upliftment Project in Mumbai (also called the Bhendi Bazaar
Project) and the redevelopment of East Kidwai Nagar in New Delhi being undertaken by the National Building Construction Corporation.
- Greenfield development will introduce most of the Smart Solutions in a previously vacant area (more than 250 acres) using innovative planning,
plan financing and plan implementation tools (e.g. land pooling/ land reconstitution) with provision for affordable housing, especially for the
poor. Greenfield developments are required around cities in order to address the needs of the expanding population. One well known example is
the GIFT City in Gujarat. Unlike retrofitting and redevelopment,
- Greenfield developments could be located either within the limits of the ULB or within the limits of the local Urban Development Authority (UDA).
- Pan-city development envisages application of selected Smart Solutions to the existing city-wide infrastructure. Application of Smart Solutions
will involve the use of technology, information and data to make infrastructure and services better. For example, applying Smart Solutions in
the transport sector (intelligent traffic management system) and reducing average commute time or cost of citizens will have positive effects
on productivity and quality of life of citizens. Another example can be waste water recycling and smart metering which can make a huge contribution
to better water management in the city.
The smart city proposal of each shortlisted city is expected to encapsulate either a retrofitting or redevelopment or greenfield development
model, or a mix thereof and a Pan-city feature with Smart Solution(s). It is important to note that pan-city is an additional feature to be provided.
Since smart city is taking a compact area approach, it is necessary that all the city residents feel there is something in it for them also. Therefore,
the additional requirement of some (at least one) city-wide smart solution has been put in the scheme to make it inclusive.
For North Eastern and Himalayan States, the area proposed to be developed will be one-half of what is prescribed for any of the
alternative models - retrofitting, redevelopment or greenfield development.