Publications
Archived datasets used in publications can be found here
- The Internet Topology Zoo Simon Knight, Hung X. Nguyen, Nickolas Falkner, Rhys Bowden, Matthew Roughan to appear in IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 29, NO. 9, OCTOBER 2011;
- Planarity of Data Networks Rhys Bowden, Hung X. Nguyen, Nickolas Falkner, Simon Knight, Matthew Roughan; to be presented at International Telecommunications Conference, September, 2011, San Francisco.
- Realistic Network Topology Construction and Emulation from Multiple Data Sources Simon Knight, Hung Nguyen, Nickolas Falkner, Matthew Roughan; Technical Report, May, 2012


Parks List
National Parks 1
Arches
Badlands
Carlsbad Caverns
Denali
Everglades
State Parks of Texas
Abilene
Big Bend
Choke Canyon
Davis Mountains
Enchanted Rock
Nation Parks 2
Arches
Badlands
Carlsbad Caverns
Denali
Everglades


We collected over 1,280,000 free text complaints from over 300 towns and cities in order to fine tune our model. Some of the largest stores of 311 complaint data came from open data portals of cities such as Cincinatti, Chicago, and Baton Rouge. Other data came from public 311-type complaint portals.
Each complaint was tagged with the primary department in charge of handling the complaint, as well as a sub-department and/or a specific complaint category. This data were used to create a list of possible responsibility hierachies. We analyzed the hierarchies and created a new unifying organization of MAIN category and SUB category to which each hierachy could be mapped.
Complaints contained a wide range of additional data such as submission timestamp, resolution timestamp, incident GPS or address, and submission method. These were not used in the classification model but serve as useful data for future exploration.
Each SUB category is directly dependent upon a MAIN category. In order to simplify the classification process, we designed the model to predict the SUB category, since complaints can be easily re-routed within the same MAIN category if the model misclassifies a complaint.
Not all of the free-text complaints were representative of how a Buddy311 would submit a complaint. Many text complaints were a series of service codes, while others were partly or entirely automatically generated messages from the respective 311 system.
After removing numerical service codes and automated system messages, we were left with around 960,000 clear free-text comments to train and test the model.
Good cosas
stuff
ipsum lorem
The model we use to classify complaints is based on an open-source pre-trained transformer model called Finetune, developed by OpenAI and Indico. It is built upon TensorFlow and currently has an accuracy of 85% on complaint sub-classes and 89% on complaint main classes.
Finetune is a task-agnostic semi-supervised Natural Language Processing algorithm. Word representations are pre-trained on a very large corpus of media of different styles and subjects to obtain a generalized representation of the English language. The pre-trained language model can predict the next word in a sentence, given all the words that came before it. Finetune uses the “Multi-Layer Transformer Decoder” model architecture, which is a deep-learning model that utilizes the concept of attention to selectively focus on different parts of the input while training. For this step, Finetune trained their model on the BooksCorpus dataset, which contains seven thousand unique books.
For our baseline models we used a multi-class Linear SVM from the sklearn python package and FastText, an NPL package provided by Facebook. While both baselines performed well, our final model based on Finetune did perform that best and is our foundational model for the Buddy311 complaint classifier.
FastText is a pre-trained continuous bag of words model developed by Facebook that uses character n-grams of 2-6 characters length to represent each word. Classification models can be trained on wordNgram of 1-5 words long.
Our state of the art model for classifying 311 complaints. Our Buddy311 fine-tuned transformer model performed the best of all of our tested models: 85% on sub-classes and 89% on main-classes.
A Transformer network is a type of artificial intelligence neural network that can be trained to take in certain inputs (i.e. text), encode the input, and then decode the given information to a specific output. This output could be the next most probable word in a sentence, the sentiment of the sentence, or in our case a classification of a complaint.
The second step of model development is the supervised fine tuning. Supervised training takes the task-appropriate text samples and true classifications to reform the pre-trained model to fit our specific task.
This website provides a basic overview of the Buddy311 system. Aside from the background information on our technology, we offer multiple user-friendly options to interact with the Buddy311 complaint classification model.
The first option is a direct connection with a Google Assistant Agent chatbot that takes user input text or speech-to-text and packages the complaint to query the model using the model's REST API. The second option is a custom Siri shortcut for updated iOS devices which is able to call the same model REST API.
Google Dialogflow allows us to:
Intents, which are triggered on specific phrases said by the user. Dialogflow actually allowed us to specify a list of potential phrases the user might say, and then it trained itself to recognize variations of these phrases.
Fullfillments:
REST Server:
Siri shortcuts are user-programmable actions that expand and enhance Apple Siri capabilities.
Actions:
REST Server:
Behind the scenes, our back end archecture recieves complaint text and metadata from the front end interface and sends this data to our model classification queue and to our database to log the complaint.
From the graphic above, we see that the submitted complaint is sent via the OPEN311 API to our database and a Kafka client, giving a submission confirmation back to the user to ensure them that their concern is our concern. The OPEN311 API sends the complaint text and any additional metadata provided by the user to our database. At the same time, it sends the complaint text to a Kafka instance that queues complaints and periodically sends the complaint text to the Buddy311 model server for classification. When the classification is complete, the classification is stored in the system database, ready for use.
Our FineTune model is Python-based, so a Flask API would seem like an obvious choice. However, Python is single threaded, so it would not be able to handle many requests at once. We instead chose a Sanic webs erver to run the model and API.
Sanic web server:
To handle complaint text and metadata, we use the Miami Dade Open311 server. Using an open standard server has numerous benefits:
We used a MariaDB SQL database to store our complaints and their labels. MariaDB SQL database:
A Kafka instance allows us to separate our backend Open311 server and classification server by facilitating asynchronous communication between them. By separating the classification server from the backend Open311 server we avoided having our backend server lock up and wait for something to get classified before doing anything else. The Kafka Instance:


Script interface.
More...
#include <Jinx.h>
Script interface.
IScript represents a single script executing as compiled bytecode. Each script has its own execution stack, and so can run for an indeterminate amount of time. The user is expected to run the script by calling Execute() each update tick until it's finished executing. Check to see if the script is finished executing using the IsFinished() function.
◆ CallAsyncFunction()
| virtual CoroutinePtr Jinx::IScript::CallAsyncFunction |
( |
RuntimeID |
id, |
|
|
Parameters |
params |
|
) |
| |
|
pure virtual |
Call a library function as a coroutine.
- Parameters
-
| id | RuntimeID of the async function to call |
| params | Vector of Variants to act as function parameters |
- Returns
- Returns the ICoroutine interface to query when the function is finished executing and to retrieve the return value.
◆ CallFunction()
| virtual Variant Jinx::IScript::CallFunction |
( |
RuntimeID |
id, |
|
|
Parameters |
params |
|
) |
| |
|
pure virtual |
Call a library function.
- Parameters
-
| id | RuntimeID of the function to call |
| params | Vector of Variants to act as function parameters |
- Returns
- Returns the Variant containing the function return value, or null for no value.
◆ Execute()
| virtual bool Jinx::IScript::Execute |
( |
| ) |
|
|
pure virtual |
Execute the script.
The client is responsible for calling Execute() once per simulation tick until the script is finished executing, which is checked with the IsFinished() function.
- Returns
- true if executed successfully and false if an error has occured.
◆ FindFunction()
| virtual RuntimeID Jinx::IScript::FindFunction |
( |
LibraryPtr |
library, |
|
|
const String & |
name |
|
) |
| |
|
pure virtual |
Find the ID of a library function.
- Parameters
-
| library | Pointer to library containing function to call |
| name | String containing all function nameparts and parameters. Parameters are indicated with "{}", while names are expected to conform to standard Jinx identifier naming rules. |
- Returns
- Returns a valid RuntimeID on success, InvalidID on failure.
◆ GetLibrary()
| virtual LibraryPtr Jinx::IScript::GetLibrary |
( |
| ) |
const |
|
pure virtual |
Return the library to which this script belongs.
- Returns
- This function returns a shared pointer to the library which this script belongs to.
◆ GetName()
| virtual const String& Jinx::IScript::GetName |
( |
| ) |
const |
|
pure virtual |
Get the script name.
- Returns
- This returns the name of the script designated by the user when compiling from source.
- See also
- IRuntime::CompileScript(), IRuntime::ExecuteScript()
◆ GetUserContext()
| virtual Any Jinx::IScript::GetUserContext |
( |
| ) |
const |
|
pure virtual |
Get a user context pointer.
- Returns
- void pointer optionally passed at script creation. This is intended to be used by native library functions to retrieve user-specific data or objects.
◆ GetVariable()
| virtual Variant Jinx::IScript::GetVariable |
( |
const String & |
name | ) |
const |
|
pure virtual |
Get a variable by name.
- Parameters
-
| name | The name of the variable to retrieve |
- Returns
- Variant representing the variable to retrieve. A Variant of type ValueType::Null is returned if the variable is not found.
◆ IsFinished()
| virtual bool Jinx::IScript::IsFinished |
( |
| ) |
const |
|
pure virtual |
Checks if the script is finished executing.
Determines if the script has finished executed or hit a fatal error
- Returns
- true if finished, false if still executing.
◆ SetVariable()
| virtual void Jinx::IScript::SetVariable |
( |
const String & |
name, |
|
|
const Variant & |
value |
|
) |
| |
|
pure virtual |
Set a variable by name.
- Parameters
-
| name | The name of the variable to set |
| value | The value of the variable to set |
The documentation for this class was generated from the following file:


nips nips2009 nips2009-61 nips2009-61-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Novi Quadrianto, John Lim, Dale Schuurmans, Tibério S. Caetano
Abstract: We develop a convex relaxation of maximum a posteriori estimation of a mixture of regression models. Although our relaxation involves a semideﬁnite matrix variable, we reformulate the problem to eliminate the need for general semideﬁnite programming. In particular, we provide two reformulations that admit fast algorithms. The ﬁrst is a max-min spectral reformulation exploiting quasi-Newton descent. The second is a min-min reformulation consisting of fast alternating steps of closed-form updates. We evaluate the methods against Expectation-Maximization in a real problem of motion segmentation from video data. 1
[1] S. Gaffney and P. Smyth. Trajectory clustering with mixtures of regression models. In ACM SIGKDD, volume 62, pages 63–72, 1999.
[2] S. Finney, L. Kaelbling, and T. Lozano-Perez. Predicting partial paths from planning problem parameters. In Proceedings of Robotics: Science and Systems, Atlanta, GA, USA, June 2007.
[3] A. J. Storkey and M. Sugiyama. Mixture regression for covariate shift. In Sch¨ lkopf, editor, o Advances in Neural Information Processing Systems 19, pages 1337–1344, 2007.
[4] A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 39(1):1–38, 1977.
[5] T. De Bie, N. Cristianini, P. Bennett, and E. Parrado-hern¨ ndez. Fast sdp relaxations of graph a cut clustering, transduction, and other combinatorial problems. JMLR, 7:1409–1436, 2006.
[6] Y. Guo and D. Schuurmans. Convex relaxations for latent variable training. In Platt et al., editor, Advances in Neural Information Processing Systems 20, pages 601–608, 2008.
[7] S. M. Goldfeld and R.E. Quandt. Nonlinear methods in econometrics. Amsterdam: NorthHolland Publishing Co., 1972.
[8] D. W. Hosmer. Maximum likelihood estimates of the parameters of a mixture of two regression lines. Communications in Statistics, 3(10):995–1006, 1974.
[9] H. Sp¨ th. Algorithm 39: clusterwise linear regression. Computing, 22:367–373, 1979. a
[10] W.S. DeSarbo and W.L. Cron. A maximum likelihood methodology for clusterwise linear regression. Journal of Classiﬁcation, 5(1):249–282, 1988.
[11] P.N. Jones and G.J. McLachlan. Fitting ﬁnite mixtures models in a regression context. Austral. J. Statistics, 34(2):233–240, 1992.
[12] S. Gaffney and P. Smyth. Curve clustering with random effects regression mixtures. In AISTATS, 2003.
[13] M.I. Jordan and R.A. Jacobs. Hierarchical mixtures of experts and the em algorithm. Neural computation, 6:181–214, 1994.
[14] S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004.
[15] M. Overton and R. Womersley. Optimality conditions and duality theory for minimizing sums of the largest eigenvalues of symmetric matrices. Mathematical Programming, 62:321–357, 1993.
[16] J. Yu, S.V.N. Vishwanathan, S. G¨ nter, and N. Schraudolph. A quasi-Newton approach to u nonsmooth convex optimization. In ICML, 2008.
[17] A. Argyriou, T. Evgeniou, and M. Pontil. Convex multi-task feature learning. Machine Learning, 73:243–272, 2008.
[18] J. Chen, L. Tang, J. Liu, and J. Ye. A convex formulation for learning shared structures from multiple tasks. In ICML, 2009.
[19] N.Vasconcelos and A. Lippman. Empirical bayesian em-based motion segmentation. In CVPR, 1997.
[20] P. Torr. Geometric motion segmentation and model selection. Philosophical Trans. of the Royal Society of London, 356(1740):1321–1340, 1998.
[21] R. Vidal, Y. Ma, S. Soatto, and S. Sastry. Two-view multibody structure from motion. IJCV, 68(1):7–25, 2006.
[22] H. Li. Two-view motion segmentation from linear programming relaxation. In CVPR, 2007.
[23] http://www.vision.jhu.edu/data/hopkins155/. 9


2009 NIST Language Recognition Evaluation Test Set, LDC2014S06 and ISBN 1-58563-682-7, contains approximately 215 hours of conversational telephone speech and radio broadcast conversation collected by the Linguistic Data Consortium (LDC) in the following 23 languages and dialects: Amharic, Bosnian, Cantonese, Creole (Haitian), Croatian, Dari, English (American), English (Indian), Farsi, French, Georgian, Hausa, Hindi, Korean, Mandarin, Pashto, Portuguese, Russian, Spanish, Turkish, Ukrainian, Urdu and Vietnamese.
The goal of the NIST (National Institute of Standards and Technology) Language Recognition Evaluation (LRE) is to establish the baseline of current performance capability for language recognition of conversational telephone speech and to lay the groundwork for further research efforts in the field. NIST conducted language recognition evaluations in 1996, 2003, 2005 and 2007. The 2009 evaluation increased the number of target languages. Most of the test data originated from multilingual Voice of America (VOA) radio broadcasts assessed as being of telephone bandwidth in addition to conversational telephone speech. Further information regarding this evaluation can be found in the evaluation plan which is included in the documentation for this release.
LDC released the prior LREs as:
The VOA speech data was collected by LDC in 2000 and 2001 and constitutes approximately 75% of the test set. The telephone speech was taken from LDC's Mixer 3 collection recorded between 2005 and 2007.
All test speech segments are presented as a sampled data stream in standard 8-bit 8-kHz μ-law format. Each segment is stored separately in a single channel SPHERE format file.
The test segments contain three nominal durations of speech: 3 seconds, 10 seconds and 30 seconds. Actual speech durations vary, but were constrained to be within the ranges of 2-4 seconds, 7-13 seconds and 23-35 seconds, respectively. Non-speech portions of each segment were included in each segment so that a segment contained a continuous sample of the source recording. Therefore, the test segments may be significantly longer than the speech duration, depending on how much non-speech was included.
Please see file.tbl for a complete file list as well as checksums for this publication.
Additional information, updates, bug fixes may be available in the LDC catalog entry for this corpus at LDC2014S06.
Portions © 2000, 2001, 2005-2007, 2014 Trustees of the University of Pennsylvania


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A quality PDP flag for campaign rallies. - Total (USD) $20


--- title: Blog description: Paul Foster has a blog now. A place for new projects, code demos and general web musings to appear for you to read. layout: default --- {% if paginator.page == 1 %}
A place for new projects, code demos and general webby musings to appear for you to read. Subscribe to my posts via RSS.
{% endif %} {% for post in paginator.posts %}{{ post.excerpt | strip_html | default: post.description }}


Photos: Ranbir Kapoor's dinner outing with Rishi Kapoor, Neetu Kapoor and Riddhima
by TIMESOFINDIA.COMRanbir Kapoor has several films lined up ahead and on Tuesday night the actor time off from his busy schedule to be with his family.
The actor was snapped as he stepped out with his father Rishi Kapoor, mother Neetu Kapoor, sister Riddhima Kapoor Sahni with her daughter. It was the first time that the whole family was snapped together.
For the night, Ranbir and Rishi were twinning in blue while Neetu opted for a black and Riddhima was in pink. The family was all smiles for the pictures. The 'Jagga Jassoos' actor looked cool as he was sporting glasses.
Check out the pictures here:
On the work front, the actor will be next seen in Ayan Mukerji's mythological drama 'Brahmastra' co-starring Alia Bhatt and Amitabh Bachchan. The film has been slated for January 2020 release. Ranbir, on the other hand, will star in a film with Sanjay Dutt apart from Ayan's mythological drama.


Hello I am Emre Usun, I am interesting on photography for long time and this website is about mypersonal live and my works. In this personal website you can see my see world from my camera angle!
Statue Photo
Sunrise photo
Grey Tree Photo
Pigeon photo


|
Bitstream Interpretation Library (BIL)
0.1
|
Holds a configuration frame of a Virtex-5 device. More...
#include <configuration/V5CfgFrame.hpp>
Public Member Functions | |
| V5CfgFrame () | |
| Constructs a new V5CfgFrame instance. | |
| void | reset () |
| Zero out frame data. | |
| boost::uint32_t * | data () |
| Gets a pointer to the frame data. | |
| const boost::uint32_t * | data () const |
| Gets a read only pointer to the frame data. |
Static Public Attributes | |
| static const size_t | WORDCOUNT = 41 |
| Virtex-5 frames are 41 words big. | |
| static const size_t | HCLK_WORD_INDEX = 20 |
| Index of the HCLK word in the frame. |
Holds a configuration frame of a Virtex-5 device.
A frame is the smallest unit of configuration data that can be written to configuration memory at once. Its destination is determined by the frame address.
Definition at line 23 of file V5CfgFrame.hpp.
| V5CfgFrame::V5CfgFrame | ( | ) |
Constructs a new V5CfgFrame instance.
Definition at line 11 of file V5CfgFrame.cpp.
| void V5CfgFrame::reset | ( | ) |
Zero out frame data.
This is default configuration for unused logic: A completely unused FPGA will have all frames zeroed out.
Definition at line 17 of file V5CfgFrame.cpp.
| boost::uint32_t * V5CfgFrame::data | ( | ) |
Gets a pointer to the frame data.
Definition at line 23 of file V5CfgFrame.cpp.
| const boost::uint32_t * V5CfgFrame::data | ( | ) | const |
Gets a read only pointer to the frame data.
Definition at line 29 of file V5CfgFrame.cpp.
|
static |
Virtex-5 frames are 41 words big.
Definition at line 53 of file V5CfgFrame.hpp.
|
static |
Index of the HCLK word in the frame.
A Virtex-5 frame consists of three parts: 20 lower words, 1 HCLK word, and 20 upper words.
Definition at line 60 of file V5CfgFrame.hpp.


GBEmulator
Émulateur de GameBoy™ classique
src
Memory
Classes
|
Namespaces
Memory.hpp File Reference
#include <vector>
Include dependency graph for Memory.hpp:
This graph shows which files directly or indirectly include this file:
Go to the source code of this file.
Classes
class
GBEmulator::Memory::Memory
Représente un morceau de mémoire.
More...
Namespaces
GBEmulator
GBEmulator::Memory
Generated by
1.8.17


ALERTS & WELLS
Alerts
Alert! The European languages are members of the same family.
Alert! The European languages are members of the same family.
Alert! The European languages are members of the same family.
Alert! The European languages are members of the same family.
Alerts with icons
Alert! The European languages are members of the same family.
Alert! The European languages are members of the same family.
Alert! The European languages are members of the same family.
Alert! The European languages are members of the same family.
Wells
Well! The European languages are members of the same family.
Well! The European languages are members of the same family.
Well! The European languages are members of the same family.


|
Function |
Reports soldermask clearances that came too close to both
via and plated holes, or where the original soldermask clearance is drilled
out. The SM opening is enlarged and the minimum distance from soldermask edge
to drill hole edge is maintained, according to the following ERF variables: v_grow_small_pads_via
and v_grow_small_pads_pth
The measurements represent the distance from the edge of
the via or plated holes to the edge of the soldermask clearance. In all cases where the soldermask clearance is larger than
the signal layer pad, the Soldermask Optimization DFM action rules are not
violated. See examples below under Gasket Removed Category |
|
Activation |
|
The following examples represent conditions that are caused by
the values assigned to three ERF variables that determine the measurements
reported in the Gasket Removed category:
v_do_small_clearances
v_grow_small_pads_pth
v_grow_small_pads_pth
v_do_small_clearances
= no
v_grow_small_pads_via
= 5
v_grow_small_pads_pth
= 3
Sizes
Signal layer pad with size = 60,
Drill pad size 40 with .drill= via attribute
SM clearance pad with size = 35
Results
After SMO, SM clearance pad will have size = 50
Sizes
Signal layer pad with size = 60
Drill pad size 40 with .drill= via attribute
SM clearance pad with size = 42
Results
After SMO, SM clearance pad will have size = 50
Sizes
Signal layer pad with size = 60
Drill pad size 40 with .drill= pth attribute
SM clearance pad with size = 40
Results
After SMO, SM clearance pad will have size = 46
v_do_small_clearances
= yes
v_grow_small_pads_via
= 5
v_grow_small_pads_pth
= 3
Sizes
Signal layer pad with size = 60
Drill pad size 40 with .drill= via attribute
SM clearance pad with size = 35
Results
After SMO, SM clearance pad will have size = 35
Sizes
Signal layer pad with size = 60
Drill pad size 40 with .drill= via attribute
SM clearance pad with size = 42
Results
After SMO, SM clearance pad will have size = 42
Sizes
Signal layer pad with size = 60
Drill pad size 40 with .drill= pth attribute
SM clearance pad with size = 40
Results
After SMO, SM clearance pad will have size = 40
v_do_small_clearances
= no
v_grow_small_pads_via
= -1 ( or not present )
v_grow_small_pads_pth
= 3
Sizes
Signal layer pad with size = 60
Drill pad size 40 with .drill= via attribute
SM clearance pad with size = 35
Results
After SMO, SM clearance pad will have size = 35
Sizes
Signal layer pad with size = 60
Drill pad size 40 with .drill= via attribute
SM clearance pad with size = 42
Results
After SMO, SM clearance pad will have size = 42
Sizes
Signal layer pad with size = 60
Drill pad size 40 with .drill= pth attribute
SM clearance pad with size = 40
Results
After SMO, SM clearance pad will have size = 46


Temple ACM is dedicated to expanding the academic and professional knowledge of computing across campus. We are also dedicated to helping our students and faculty to cooperate and grow together. Our entire organization is fueled by its collective passion for the boundless possibilities of emerging technology and the creativity it inspires. The world of computing invokes a different perspective on the world and sparks the imagination of its students. We are proud to be evangelists of our craft, and hope to create some incredible technologists.
Read More »Temple ACM welcomes people of all majors and creeds to meetings to encourages exploration and discussion of a wide variety of computing-related ideas. We also contend to connect students not only with a vast network of companies, but also to the ever-growing community of their peers. As students, we have a unique opportunity to explore that is rare later in life, and we believe that attending events like hack-a-thons and conferences as a group encourages growth of programming know-how and camraderie.
Read More »Temple ACM is dedicated to the idea that innovation is the key driver of technology. That technology is a key piece of our everyday lives is already evident, but pushing technology forward is all about figuring out improvements to the human experience and pursuing them. We try to emphasize the value of brainstorming at meetings and have a great culture around communal support for ideas within the group, but understand that as the next generation of developers we must be the harbringers of the next generation of ideas - for better of for worse.
Read More »Temple ACM maintains active and storied connections to businesses in our vicinity, especially from Philadelphia. We think that, as computing students, we have a responsibility to help our homegrown businesses flourish with internships and open source efforts. As a result, we like to have local businesses bring representatives to talk to our delegation. This creates an interest in students and an opportunity for businesses to acquire fresh talent.
Read More »Temple ACM is dedicated to the open source movement and the technology thereof. We think that socializing the software process is a beautiful. So, naturally, we have a github organization where we try collaborate on new projects as a club. We think organizations like the Eclipse Foundation and Apache Software Foundation represent an incredible vista for our industry’s future, and, appropriately, we try to employ open source tools and platforms whenever possible.
Read More »


Exploring hardware, software, and everywhere in-between.
Tim Growney is a CTO at Medinas by day, Serverless enthusiast by night. Interests include scuba, biochemistry, and toying with Raspberry Pis.


Before Change
fname = get_fnames("fornix")
streams, hdr = tv.read(fname)
streamlines = [i[0] for i in streams]
Perform QuickBundles clustering using our metric ``CosineMetric``.
from dipy.segment.clustering import QuickBundles
metric = CosineMetric()
qb = QuickBundles(threshold=0.1, metric=metric)
clusters = qb.cluster(streamlines)
We will now visualize the clustering result.
// Color each streamline according to the cluster they belong to.
colormap = actor.create_colormap(np.arange(len(clusters)))
colormap_full = np.ones((len(streamlines), 3))
for cluster, color in zip(clusters, colormap):
colormap_full[cluster.indices] = color
ren = window.Renderer()
ren.SetBackground(1, 1, 1)
After Change
from dipy.viz import window, actor
fname = get_fnames("fornix")
fornix = load_tractogram(fname, "same",
bbox_valid_check=False).streamlines
streamlines = Streamlines(fornix)
Perform QuickBundles clustering using our metric ``CosineMetric``.
from dipy.segment.clustering import QuickBundles
metric = CosineMetric()
qb = QuickBundles(threshold=0.1, metric=metric)
clusters = qb.cluster(streamlines)
We will now visualize the clustering result.
// Color each streamline according to the cluster they belong to.
colormap = actor.create_colormap(np.arange(len(clusters)))
colormap_full = np.ones((len(streamlines), 3))
for cluster, color in zip(clusters, colormap):
colormap_full[cluster.indices] = color
ren = window.Renderer()
ren.SetBackground(1, 1, 1)


HTML is the primary language of the web. HTML stands for HyperText Markup Language.
In other to create a website, you need to first learn how to write HTML codes.
This cheat sheet - or HTML code quick reference - lists the common HTML tags and their attributes, grouped into relevant sections in an easy-to-read format.
What is html? What are tags? Read on to learn about them.
The structure starts with HTML are:
- html
- title
- header
See an example below.
Each tags comes in pairs with their closing tags with a few exceptions that are self closing.

<title>website title</title>

content of website ...

- h1
- li
- ul
- ol
- img
- hr
- br
We have the heading tag (

) - it can have values from 1 to 6
We have the paragraph tag (

) and many other tags.
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{% extends 'base.html' %}
{% load static%}
{% block content %}
This style is most common among women. Bouffant. A style characterized by smooth hair .
View HairstyleWe offer the best acrylix paints of every design and pattern depending on your choice.
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Studiocanal’s Upcoming 4K Releases Include ‘Irreversible: Straight Cut,’ ‘Dead of Night’ and ‘Total Recall’
by John SquiresA whole new wave of classic films are being restored for home video release by Studiocanal next year, and Deadline brings us a peek at the company’s 2020 lineup this week.
Deadline notes, “The library titles will get home entertainment releases in multiple Studiocanal territories and some will have theatrical play.” The lineup includes:
- Breathless
- Basic Instinct
- Dead of Night
- The Elephant Man
- Flash Gordon
- Irreversible
- The Ladykillers
- Lo Sceicco Blanco
- The Red Circle
- Serpico
- The Three Days of the Condor
- Total Recall (1990)
The version of Irreversible being released by Studiocanal next year is Gaspar Noe’s new “Straight Cut,” which premiered at the Venice Film Festival earlier this year. The “Straight Cut” re-cuts the film in chronological order, chopping five minutes off the runtime in the process. According to Noe, the “Straight Cut” is “another film” entirely. You’ll find the trailer below.


$output = $wallet->delete(array(), $apiContexts['BTC.main']);
} catch (Exception $ex) {
ResultPrinter::printError("Deleted Wallet", "Wallet",
$walletName, null, $ex);
exit(1);
}
ResultPrinter::printResult("Deleted Wallet", "Wallet",
$walletName, null, $output);
return $output;
Delete Wallet Sample
This sample code demonstrate how you can delete a wallet, as documented here at: http://dev.blockcypher.com/#wallet_api API used: DELETE /v1/btc/main/wallets/Wallet-Name


My Site
June 05, 2015.
On of the early projects given to us by DBC was to create a site where we could upload weekly blog posts among other things. The primary purpose of this was to enable us to practice HTML and CSS which we were just being introduced to at the moment. I thought this was a good challenge and I set out to create something decent. Below, is the first version of what my site looks like:
Yes... I know. It looks wonderful and extremely creative. This was my first attempt and I think it looks good. After looking around and doing a bit of research, I decide to change the color scheme of the site and this is what it became:
While doing some site maintenance after getting a couple of feedbacks, I realized that the navbar and a few other things could be improved, so I made a few changes, and it came out to be this:
After spending so many hours, I thought it looked pretty decent. I was quite satisfied with what I had made because I used just HTML and CSS to achieve all that.
This week, we were asked to access our site to see if we could make it look more professional. Some suggestions were to implement Jekyll, or use Bootstrap to make things look nicer. I had never used any of these so I had to do some research and try to implement something. I decided to use Bootstrap and below is the outcome:
I think it looks alright. Bootstrap is a popular HTML, CSS, and JavaScript framework for making responsive websites and also to make CSS less painful. It made a few things look nicer, and I also decided to change the color once more.
Implementing bootstrap was initially challenging because I had never used it before. I had to do some research to understand the basics. I went over some tutorials from W3schools and I also looked at some samples on the Bootstrap website. After a couple of hours, I was set to apply it to my site. Another challenging part of making changes to my site was going through at least 16 different files and making individual changes to all of them because I was attempting to redesign the whole thing using bootstrap. After hours of testing, and making changes, I was reasonably successful.
The whole process is a good learning experience for me. When I look back at previous versions that I had (thanks to git), I see the progress that I've made through out the different versions. I think it is quite encouraging to know that things are actually progressing and it doesn't matter how small each progression might be.


| 028:019 Khan | : |
Then when he decided to seize the man who was an enemy to both of them, the man said: "O Musa (Moses)! Is it your intention to kill me as you killed a man yesterday?   Thou only desirest to be a tyrant in the land, and thou desirest not to be of those who act aright. |
| 028:019 Pickthal | : |
And when he would have fallen upon the man who was an enemy unto them both, he said: O Moses!   Obviously, you wish to be a tyrant on earth; you do not wish to be righteous." |
| 028:019 Sarwar | : |
When Moses was about to attack their enemy, he said, "Moses, do you want to kill me as you slew a soul the other day? Do you want to become a tyrant in the land, not a reformer?"   Thou only desirest to be a tyrant in the land, and thou desirest not to be a peace-maker.' |
| 028:019 Yusufali | : |
Then, when he decided to lay hold of the man who was an enemy to both of them, that man said: "O Moses! Is it thy intention to slay me as thou slewest a man yesterday? Thy intention is none other than to become a powerful violent man in the land, and not to be one who sets things right!" |
Concepts/Themes Covered:
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


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new Adapter(wiki)
This library provides all the functions to interact with the TW as if it was a simple database for nodes and edges. It may be understood as an abstraction layer.
Everything that is related to retrieving or inserting nodes and edges is handled by the adapter.
Parameters:
| Name | Type | Description |
|---|---|---|
wiki |
object | An optional wiki object |
- Source:
Methods
-
_restorePositions(nodes)
-
This method will load the current view's map values into the nodes
Parameters:
Name Type Description nodes
object A hashmap of node-objects.
- Source:
-
createView(label) → {ViewAbstraction}
-
Create a view with a given label (name).
Parameters:
Name Type Argument Default Description label
string <optional>
"My View" The name of the view (not a TiddlerReference).
- Source:
Returns:
The newly created view.
- Type
- ViewAbstraction
-
deleteEdgesFromStore(edges)
-
Removes multiple edges from several stores.
Parameters:
Name Type Description edges
EdgeCollection The edges to be deleted
- Source:
-
deleteNodesFromStore(nodes)
-
This function will remove all tiddlers from the wiki that correspond to a node in the collection. Drafts are also removed. The default storylist is updated eventually.
Parameters:
Name Type Description nodes
NodeCollection A collection of nodes.
- Source:
-
filterEdgesByEndpoints(edges, nodes, options) → {EdgeCollection}
-
This function will return a set of edges, which only contains edges that possess endpoints ("from" or "to") which are contained in the nodes set.
Parameters:
Name Type Argument Description edges
EdgeCollection The edges to filter.
nodes
NodeCollection The endpoints.
options
Hashmap <optional>
An optional options object.
Properties
Name Type Argument Default Description endpointsInSet
"=1" | ">=1" | "=2" <optional>
">=1" How many endpoints do have to match for the edge to be accepted? Per default, at least one edge has to match. Setting
endpointsInSet
to "=1" or "=2" means exactly one or respectively two endpoints have to match.outputType
CollectionTypeString <optional>
"dataset" The result type.
- Source:
Returns:
A collection of a type specified in the options.
- Type
- EdgeCollection
-
getView(view)
-
Function to create or abstract a view from outside.
Parameters:
Name Type Description view
View The view.
- Source:
-
insertEdge(edge, view)
-
Persist an edge with at least a given id, from and to label in the TW.
If an edge is lacking an id, it will be created. The reason to give edges an id instead of merely relying on from, to and the type to identify an edge is twofold: First, the same vector (from, to) may exist multiple times, second, the id is needed to interact with vis in a comfortable way because vis ensures only that ids are unique in each dataset.
Parameters:
Name Type Argument Description edge
Edge The edge to be saved
view
View <optional>
An optional view to determine which prefix to use.
- Source:
-
insertNode(node, options)
-
Create a new tiddler that gets a non-existant title and is opened for edit. If a view is registered, the fields of the tiddler match the current view. If arguments network and position are specified, the node is also inserted directly into the graph at the given position.
 Options include:
- editNodeOnCreate: True, if the node should be opened in edit mode after it was created, false otherwise. Overwrites the global default - view: a viewname used to set positions and register the node to
- Source:
-
selectEdgesByEndpoints(nodes, options) → {EdgeCollection}
-
Selects edges that are connected to nodes in a set either by their from-part or by their to-part.
Parameters:
Name Type Argument Description nodes
NodeCollection A set of nodes.
options
Hashmap <optional>
An optional options object.
Properties
Name Type Argument Description !! INHERITED !!
Hashmap <optional>
!! INHERITED !!
Hashmap <optional>
- Source:
Returns:
A collection of a type specified in the options.
- Type
- EdgeCollection
-
selectEdgesByFilter(filter, options) → {EdgeCollection}
-
Returns a set of edges that corresponds to the given filter.
Parameters:
Name Type Argument Description filter
TiddlyWikiFilter The filter to apply.
options
Hashmap <optional>
An optional options object.
Properties
Name Type Argument Description !! INHERITED !!
Hashmap <optional>
- Source:
Returns:
A collection of a type specified in the options.
- Type
- EdgeCollection
-
selectEdgesByLabel(labels, options) → {EdgeCollection}
-
Returns a set of edges where the edge labels match the labels specified.
Parameters:
Name Type Argument Description labels
array.<string> A set of labels (just the labels without any edge prefixes).
options
Hashmap <optional>
An optional options object.
Properties
Name Type Argument Default Description outputType
CollectionTypeString <optional>
"dataset" The result type.
view
View <optional>
A view that is used to filter private edges.
- Source:
Returns:
A collection of a type specified in the options.
- Type
- EdgeCollection
-
selectNeighbours(nodes, options) → {NodeCollection}
-
This function will return all direct non-dublicate neighbours with respect to the given set of nodes. If a node is a direct neighbour of another node in the set and also cointained in the set itself, it won't be added to the result set.
To calculated any degree of neighbourship, simple call this function on its own result set.
Parameters:
Name Type Argument Description nodes
NodeCollection The nodes whose neighbours will be retrieved.
options
Hashmap <optional>
An optional options object.
Properties
Name Type Argument Description !! INHERITED !!
Hashmap <optional>
!! INHERITED !!
Hashmap <optional>
edges
EdgeCollection <optional>
A collection of edges that may be provided as source instead of considering all edges in the system. The
view
option will not have an effect then.- Source:
Returns:
A collection of neighbour nodes
- Type
- NodeCollection
-
selectNodeById(id, options) → {Node|undefined}
-
Select a single node by id.
Parameters:
Name Type Argument Description id
Id A node's id
options
Hashmap <optional>
An optional options object.
Properties
Name Type Argument Description !! PARTLY INHERITED !!
Hashmap <optional>
Except from the outputType option, all options are inherited from Adapter#selectNodesByIds.
- Source:
Returns:
A node or nothing.
- Type
- Node | undefined
-
selectNodesByFilter(filter, options) → {NodeCollection}
-
Returns a set of nodes that corresponds to the given filter.
Parameters:
Name Type Argument Description filter
TiddlyWikiFilter The filter to use.
options
Hashmap <optional>
An optional options object.
Properties
Name Type Argument Description !! INHERITED !!
Hashmap <optional>
- Source:
Returns:
A collection of a type specified in the options.
- Type
- NodeCollection
-
selectNodesByIds(nodeIds, options) → {NodeCollection}
-
Retrieve nodes based on the a list of ids that corrspond to tiddlers id fields.
Parameters:
Name Type Argument Description nodeIds
Array.<Id> | Hashmap.<Id, *> | vis.DataSet The ids of the tiddlers that represent the nodes.
options
Hashmap <optional>
An optional options object.
Properties
Name Type Argument Default Description outputType
CollectionTypeString <optional>
"dataset" The result type.
view
View <optional>
A viewname used to retrieve positions
addProperties
Hashmap <optional>
a hashmap containing properties to be added to each node. For example:
{ group: "g1", color: "red" }
- Source:
Returns:
A collection of a type specified in the options.
- Type
- NodeCollection
-
selectNodesByReference(tiddlers, options) → {NodeCollection}
-
Returns a set of nodes that corresponds to a set of tiddlers.
Parameters:
Name Type Argument Description tiddlers
TiddlerCollection The tiddlers that represent the nodes
options
Hashmap <optional>
An optional options object.
Properties
Name Type Argument Default Description outputType
CollectionTypeString <optional>
"dataset" The result type.
view
View <optional>
A viewname used to retrieve positions
addProperties
Hashmap <optional>
a hashmap containing properties to be added to each node. For example:
{ group: "g1", color: "red" }
- Source:
Returns:
A collection of a type specified in the options.
- Type
- NodeCollection
-
setupTiddler()
-
This method makes sure a Tiddler has all the necessary fields that the plugin needs to work with it. This means particularly that a Tiddler has a proper value in its designated id field. The idField may be changed by the user and can also be the title field. If the id field does not exist or doesn't have a value, it will be created.
- Source:
-
storePositions(positions, view)
-
This function will store the positions into the sprecified view.
Parameters:
Name Type Description positions
object A hashmap ids as keys and x, y properties as values
view
ViewAbstraction | Tiddler | string - Source:


--- layout: post title: Storyboards for a pitch date: '2015-09-18T18:30:50+02:00' categories: - Storyboards picture_url: https://film-Storyboards.tumblr.com/post/129380107388/Storyboards-for-a-pitch ---
Storyboard for a pitch.


Last updated: 2020-06-10
Checks: 6 1
Knit directory: mcfa-para-est/
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On this page, we describe the methods that are used for decomposing the effects on parameter estimations in terms of different estimates of bias with respect to standard errors.
First, we need to compute what the true'' standard errors are with respect to each parameter. We have generated a sampling distribution for each parameter in our study, and so can use this sample distribution to compute an
empirical standard error.’’ The empirical standard error is simply the standard deviation of the sampling distribution of the parameter (in that specific condition) (Bandalos & Gagné, 2012). Which we will call \(SE(\hat{\theta}_i)\), the empirical standard error of the \(i\)-th parameter.
The bias of standard errors is formally computed as
\[ Bias\left(SE(\hat{\theta}_i)\right) = \sum_{j=1}^{n_r}\left(\frac{\widehat{SE}(\hat{\theta}_i)_j- SE(\hat{\theta}_i)}{SE(\hat{\theta}_i)}\right)/n_r\times 100 \] where, \(\widehat{SE}(\hat{\theta}_i)_j\) is the estimated standard error of the \(i\)-th parameter in the \(j\)-th replication. We will follow the suggestion of Hoogland and Boomsma (1998) that bias \(\leq \mid 5\%\mid\) is considered an ``acceptable’’ level of bias.
Bias will be estimated for
Another aspect of the standard error estimates that was of interest was the variability in standard error estimates between estimation methods. Meaning, which estimation method was least variable relative to the other methods. We compute an efficiency ratio (or relative efficiency) between estimation methods (m = MLR, u = ULSMV, and w = WLSMV). \[ RE = \sqrt{\left(\frac{\sum_{\forall j}(\widehat{SE}(\hat{\theta}_i)_{mj}- SE(\hat{\theta}_i))^2}{\sum_{\forall j}(\widehat{SE}(\hat{\theta}_i)_{uj}- SE(\hat{\theta}_i))^2}\right)} \] where RE = relative efficiency, and the ratio was computed for all three pairwise comparisons of (m, u, w).
Here, we estimated the bias estimates.
First, we set up some functions to compute the values of interest.
#compute RB
# p = parameter estimate of interest
# pt = true value of parameter of interest
est_relative_bias <- function(data, p, pt){
nr <- nrow(data)
data[, pt] <- as.numeric(data[,pt, drop=T])
rb <- sum((data[, p] - data[, pt])/data[,pt], na.rm = T)/nr*100
names(rb) <- 'RB'
return(rb)
}
# compute RMSE components
est_rmse <- function(data, p, pt){
nr <- nrow(data)
data[, pt] <- as.numeric(data[,pt, drop=T])
est_a <- mean(data[,p, drop=T], na.rm = T)
bias <- (est_a - data[,pt, drop=T][1])**2
sv <- sum((data[, p, drop=T] - est_a)**2, na.rm=T)/nr
rmse <- bias + sv
out <- c(rmse, bias, sv)
names(out) <- c('RMSE', 'Bias', 'SampVar')
return(out)
}
# compute estimated relative efficiency of
# parameter est between two estimation methods
est_relative_efficiency <- function(data, p, pt, est1, est2){
dat1 <- filter(data, Estimator == est1)
dat2 <- filter(data, Estimator == est2)
dat1[,pt] <- as.numeric(dat1[,pt, drop=T])
dat2[,pt] <- as.numeric(dat2[,pt, drop=T])
re <- sqrt(sum( (dat1[, p, drop=T] - dat1[, pt, drop=T])**2, na.rm=T)/sum( (dat2[, p, drop=T] - dat2[, pt, drop=T])**2, na.rm=T))
names(re) <- 'RE'
return(re)
}
Next, loop through the desired results to get the estimates of bias of interest. For more details on the naming of variables for the true values, see the Data Cleaning page.
source(paste0(getwd(),"/code/load_packages.R"))
source(paste0(getwd(),"/code/get_data.R"))
# take out non-converged/inadmissible cases
sim_results <- filter(sim_results, Converge==1, Admissible==1)
# set up vectors of variable names
pvec <- c(paste0('selambda1',1:6), paste0('selambda2',6:10), 'sepsiW12','sepsiB1', 'sepsiB2', 'sepsiB12', paste0('sethetaB',1:10))
# Compute empirical standard errors
sim_results <- sim_results %>%
group_by(Condition, Estimator) %>%
mutate(EmpSElambda11 = sd(lambda11), EmpSElambda12 = sd(lambda12),
EmpSElambda13 = sd(lambda13), EmpSElambda14 = sd(lambda14),
EmpSElambda15 = sd(lambda15), EmpSElambda16 = sd(lambda16),
EmpSElambda26 = sd(lambda26), EmpSElambda27 = sd(lambda27),
EmpSElambda28 = sd(lambda28), EmpSElambda29 = sd(lambda29),
EmpSElambda210 = sd(lambda210),
EmpSEpsiW12 = sd(psiW12), EmpSEpsiB1 = sd(psiB1),
EmpSEpsiB2 = sd(psiB2), EmpSEpsiB12 = sd(psiB12),
EmpSEthetaB1 = sd(thetaB1), EmpSEthetaB2 = sd(thetaB2),
EmpSEthetaB3 = sd(thetaB3), EmpSEthetaB4 = sd(thetaB4),
EmpSEthetaB5 = sd(thetaB5), EmpSEthetaB6 = sd(thetaB6),
EmpSEthetaB7 = sd(thetaB7), EmpSEthetaB8 = sd(thetaB8),
EmpSEthetaB9 = sd(thetaB9), EmpSEthetaB10 = sd(thetaB10))
# vector of empirical SE names
# set up vectors of variable names
ptvec <- c(paste0('EmpSElambda1',1:6), paste0('EmpSElambda2',6:10), 'EmpSEpsiW12','EmpSEpsiB1', 'EmpSEpsiB2', 'EmpSEpsiB12', paste0('EmpSEthetaB',1:10))
# iterators - conditions
N1 <- unique(sim_results$ss_l1)
N2 <- unique(sim_results$ss_l2)
ICC_LV <- unique(sim_results$icc_lv)
ICC_OV <- unique(sim_results$icc_ov)
EST <- unique(sim_results$Estimator)
# results matrix
result <- data.frame(matrix(nrow=length(N1)*length(N2)*length(ICC_LV)*length(ICC_OV)*length(pvec)*length(EST), ncol=(17)))
colnames(result) <- c('N1', 'N2', 'ICC_LV' ,'ICC_OV', 'Variable', 'Estimator','EmpSE', 'RB', 'RMSE', 'Bias', 'SampVar', 'muRE', 'mwRE', 'uwRE', 'nRep', 'estMean', 'estSD')
j <- 1 # row id
for(n1 in N1){
for(n2 in N2){
for(iccl in ICC_LV){
for(icco in ICC_OV){
for(i in 1:length(pvec)){
dat <- filter(sim_results, ss_l1 == n1, ss_l2==n2,
icc_lv==iccl, icc_ov==icco)
result[j:(j+2), 1:5] <- matrix(rep(c(n1,n2, iccl,icco, pvec[i]),3),
ncol=5, byrow = T)
# compute bias by each estimation method
k <- 0
for(est in EST){
sdat <- filter(dat, Estimator==est)
result[j+k, 6] <- est
result[j+k, 7] <- sdat[1, ptvec[i], drop=T]
result[j+k, 8] <- est_relative_bias(sdat, pvec[i], ptvec[i])
result[j+k, 9:11] <- est_rmse(sdat, pvec[i], ptvec[i])
result[j+k, 15] <- nrow(sdat) # number of converged replications
result[j+k, 16] <- mean(sdat[, pvec[i], drop=T])
result[j+k, 17] <- sd(sdat[, pvec[i], drop=T])
k<-k+1
}
# Compute Relative Efficiency
# MLR vs. ULSMV
result[j:(j+2), 12] <- est_relative_efficiency(dat, pvec[i], ptvec[i],
'MLR','ULSMV')
# MLR vs. WLSMV
result[j:(j+2), 13] <- est_relative_efficiency(dat, pvec[i], ptvec[i],
'MLR','WLSMV')
# ULSMV vs. WLSMV
result[j:(j+2), 14] <- est_relative_efficiency(dat, pvec[i], ptvec[i],
'ULSMV','WLSMV')
# update row
j <- j+3
}
}
}
}
}
So, there are a lot of results that could be reported from this matrix of results. We have saved these results and these estimates are included in the accompanying Shiny app for more detailed exploration by those interested. Here, we describe a subset of the results that we feel are most important.
First, we will plot estimates (boxplots) to show how these estimates changed across conditions. To summarize the results we will average over the parameters that only differ y indices. Meaning we will describe the “average factor loading bias” by reporting the average bias for factor loadings. Additionally, different conditions resultedin different “sample sizes.” By this we mean the number of uses replications. The different number of cases per condition was accounted for by creating a “weight” variable for each row of the result
object. This meant that conditions that had more usable replications counted more towards to averages reported (or count as much as if we averaged over the individual replications).
result$wi <- result$nRep/500
# 500 is the max number of replications per cell
# Save Results
write_csv(result, path=paste0(w.d, "/data/results_bias_se.csv"))
Bandalos, D. L., & Gagné, P. (2012). Simulation methods in structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 92–108). New York, NY: Guilford Press.
Hoogland, J. J., & Boomsma, A. (1998). Robustness studies in covariance structure modeling: An overview and a meta-analysis. Sociological Methods & Research, 26(3), 329-367.
sessionInfo()
R version 3.6.3 (2020-02-29)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18362)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_<IP_ADDRESS> rprojroot_1.3-2 digest_0.6.25 later_1.0.0
[5] R6_2.4.1 backports_1.1.7 git2r_0.27.1 magrittr_1.5
[9] evaluate_0.14 stringi_1.4.6 rlang_0.4.6 fs_1.4.1
[13] promises_1.1.0 rmarkdown_2.1 tools_3.6.3 stringr_1.4.0
[17] glue_1.4.1 httpuv_1.5.2 xfun_0.14 yaml_2.2.1
[21] compiler_3.6.3 htmltools_0.4.0 knitr_1.28


Author’s note: this piece is on its way to becoming a fully interactive editorial on how the game of hockey has changed since 1918, as well as how that affects point totals. For now though, scroll on to read about the project as is.
One of the biggest challenges in comparing the best modern-day players to those of previous eras is the changes in the the way the game is played. Season length, roster size, and schedule strength have all changed considerably since 1917, and goalies have gotten significantly better – the stats show us that. The stats also show us, for instance, that the average number of goals in a game spiked in the 80s. This all has to be factored in to get an accurate comparison of players between the years. Read on to see the playing field levelled and how the modern era stacks up.
The game of hockey has changed a lot in the past 100 years. Rosters have gotten bigger, the league has gotten larger, goalies have gotten better, and the rules have changed a great deal. One thing, however, has stayed pretty much constant: the way we tally goals and assists. A goal in the game last night is worth the same at the end of the season as it was in a game forty-five years ago. So why has nobody been able to compete with Wayne Gretzky or Mario Lemieux’s incredible seasons of the expansion era? Well, it’s simple. The league has gotten larger, goalies have gotten better, and the rules have changed a great deal. It’s not exactly fair to compare players between eras.
To account for this, I’ve created a series of adjustments that we can apply to every player’s statistics to even the playing field. I want to see how every player would do if they all theoretically played for the same team (separately, of course) in the average post-lockout (2004-05) season – meaning 82 games on the schedule, 18 players on a roster, 5.659 goals per game, 1.719 assists per goal, and an average save percentage of .911.
My first adjustment accounts for the difference in season length. Back in 1919/20, there were only four NHL teams and they each played twenty-four games. Now, there are thirty teams, each playing a hefty 82 games. Obviously, a shorter season correlates to a smaller point total, even for Joe Malone, who scored 48 points for the Quebec Athletic Club in 1919/20. This equates to players with shorter seasons being given a handicap, increasing their potential point total.
To even everyone out, I’ve divided the average number of games played by each team, post-lockout (not including the shortened 2012/13 season), and divided it by the average number of games played in the stat year. For example, let’s look at the first season of the expansion era, which was led by Stan Mikita of the Chicago Blackhawks. The season length in 1967/68 was 74 games. Our current 82 games divided by that length gives us a factor of 1.108. As you’d expect, a higher number means a shorter season, giving the player the aforementioned handicap. This would effectively increase his point total from 87 to 93.
Of course, it’s not as simple as just multiplying someone’s point total in their respective season by a schedule factor to bring them up to an 82-game season, no sir, it is not. If that were the case, the boys from the early years would see a huge statistical improvement while guys who played in the nineties wouldn’t see any change at all. The playing field still isn’t equal, but we’re getting there.
We also have to account for the difference in roster sizes. In 1917, teams were allowed a maximum of twelve players. It wasn’t until 1982 that roster sizes were increased to the current size of eighteen skaters and two goalies. They went up and down a few times in between, but the point is, they’ve gotten bigger. Since game length has remained at roughly the same three twenty-minute periods, this means that back in the formative years of the league, when Joe Malone scored 44 goals in one season, he actually had more ice time every game than say, Sidney Crosby does in the era of eighteen man rosters. So the guys playing in pre-1982 seasons had a slight advantage in that they had more time to score.
To make up for this, let’s divide the roster size in the stat year and divide it by the current roster size of 18. Let’s look again at 1967/68. The Black Hawks had a 16-man team, which, when divided by 18, gives us a roster factor of .889. Opposite to the schedule changes, a lower number means a small roster, which means more playing time, so a decreased point total is in order. Multiplying Mikita’s new points total of 93 by .889 lowers his total to 83.
The next adjustment address the change in playing style and goalie skill over the four main eras of the league. The Wayne Gretzkys of the high-flying 80s, playing in an era of increased scoring, poor goaltending, and weak expansion teams to prey on, obviously had an easier time scoring than the Peter Forsbergs of the recent dead-puck era, who played in a time of butterfly goaltending and the Trap. Likewise, guys who played in the league’s early years played against statistically dominant defences, but played through the introduction of icing, offsides, and forward passing, which lead to a surge in scoring by the Original Six era. To truly even out the playing circumstances, we divide the average league-wide goals per game (LGPG) in the post-lockout years by the total goals scored throughout the league (not including the player in question’s), in turn divided by the total number of games played in the league in that particular year.
Seeing as the average number of assists per goal has risen dramatically since the 20s, we do the same with assists totals. We first multiple the average league-wide assists per goal, post-lockout (LAPGoal) by the post-lockout LGPG and divide that by the total league-wide assists in the stat year, minus the player’s, divided by the games played that season.
Back to our Stan Mikita example: the league combined for 2476 goals and 4014 assists in 444 games in 1967/68, while Mikita scored 40G and 47A. The post-lockout goals-per-game is 5.657 and assists-per-goal are up at 1.719. All of this, multiplied by Mikita’s previously-adjusted point totals, ups his numbers to 41G and 50A.
What this means is that the guys playing in seasons in which more goals were scored league-wide will have their numbers reduced to more closely coincide with the number of goals scored in the modern, post-2004-2005-lockout era.
Now I know what you’re thinking, “but Sam, didn’t teams like the 1970/71 Boston Bruins and the 1985/86 Edmonton Oilers have a much easier schedule than say, the 1967/68 Chicago Blackhawks and especially the 1919/20 Quebec Athletic Club?” Well to that, I say you’re absolutely right. Teams like the Bruins in the 70s and the Oilers in the 80s had statistically easier schedules than most. That’s because of expansion. You see, expansion teams, on average, weren’t very good. They were new to the league and less established than the Original Six, and teams that joined the NHL out of the WHA were much better than teams that were new to either league. This means that teams playing in the same division as a weaker expansion team had an easier schedule than teams playing in a division without weak expansion teams, or if you get down to it, the expansion teams themselves. We measure this with a stat called Strength of Schedule, or S.O.S.. The average S.O.S. is always 0, and the lower the number, the weaker, on average, the team’s opponents., while the higher the number, the stronger, on average, the team’s opponents.
To really even the playing field and compare players through the years, we have to account for how difficult the player’s respective team’s schedule was. Wayne Gretzky playing for an Oilers team in 1985 had a much easier season than even Jamie Benn playing for the Dallas Stars in 2014/15. So we’ll factor that in too, by converting the average S.O.S. to one and multiplying it by our player’s totals and all of our other adjustments.
The results for Art Ross Trophy winners are as follows. The first column is the actual top ten seasons in the league’s history. To the right is the top ten seasons using adjusted points, not accounting for schedule strength. The third column factors in schedule strength for post-1967 seasons. If we include early seasons, Joe Malone’s 49 points in 1920 jump to a monster 211, simply because his Quebec Athletic Club, a new team, had an unheard of S.O.S. number of .90 (it was a tough year for the Bulldogs). Still, there are no modern (post-2005-lockout) players in the top ten. Joe Thornton is closest, though, jumping from 27th to 12th, with 123 points.
Doing the same for Rocket Richard Trophy winners (as it’s been called since 1998) gives us these results. Ovechkin cracks in at fourth with 67 goals (close to his actual 65 goal total), but if we factor in the Washington Capitals’ relatively easy schedule in 07/08, his total is bumped down to just 54. Steven Stamkos, on the other hand, is sitting pretty with 62.
The full visualizations are included here:
Footnotes
Created with ♥ by Sam Vickars. © 2015. All rights reserved. All trademarks property of their respective owners. Some date used herein was obtained free of charge from and is copyrighted by The Hockey Summary Project. For more information about The Hockey Summary Project, please visit this link. Other information used was obtained free of charge from and is copyrighted by The Hockey Databank Project. For more information, please visit this link.
Other sources include:
Books National Hockey League Guide (various years) National Hockey League Official Record Book (1982-83 and 1983-84) National Hockey League Official Guide & Record Book (1984-85 to present) The Stanley Cup Records and Statistics (various years) World Hockey Association Media Guide (various years) WHA Schedule & Statistics (1974-75) The Sporting News Hockey Guide (various years) Official NHL Record Book 1917-64 Total Hockey; Total Sports Publishing (1st edition, 1998; 2nd edition, 2000) The Encyclopedia of Hockey, by Robert A. Styer; A.S. Barnes (2nd edition, 1973) The Hockey Encyclopedia, by Stan Fischler and Shirley Walton Fischler; Macmillan (1983) The Trail of the Stanley Cup (Vol. 1, 2, and 3), by Charles L. Coleman
Periodicals The Sporting News
Online ESPN.com: http://www.espn.com/nhl/statistics Hockey Research Association: http://www.hockeyresearch.com/stats Hockey-Reference.com (Justin Kubatko): http://www.hockey-reference.com Internet Hockey Database (Ralph Slate): http://www.hockeydb.com Legends of Hockey.net (Hockey Hall of Fame): http://www.legendsofhockey.net/html/search.htm LostHockey.com: http://www.losthockey.com National Hockey League: http://www.nhl.com The Sports Network: http://www.sportsnetwork.com USA Today hockey stats archive: http://www.usatoday.com/sports/hockey/archive.htm


ABOUT BURSTCUBE
BurstCube Detects Gamma-ray Transients
The first direct detections of Gravitational Waves (GWs) has
brought astronomy into a new era of discovery. The search for
electromagnetic counterparts to GW sources is now more important
than ever before. BurstCube will be a 6U
CubeSat (10 cm x 20 cm x 30 cm) composed of 4 scintillator detectors
read out by arrays of silicon photomultipliers. BurstCube will
automatically detect gamma-ray transients onboard (astrophysical,
solar, and terrestrial), sending rapid alerts to the ground to
enable follow-up observations. BurstCube is currently in development and will lauch in the early 2020's.
BurstCube Effective Area
Despite the constraints of a CubeSat, BurstCube achieves an effective area of 70% of Fermi-GBM at 100 keV and 15 degree incidence. The effective area as a function of energy, and the corresponding curve for the larger Fermi-GBM NaI detectors are shown for reference.
BurstCube will increase the sky coverage for short (<2 s)
gamma-ray bursts (GRBs), especially important in the current era of GW discoveries. The
recent coincident detection of a short GRB (by Fermi and other
multiwavelength partners) and a GW trigger has provided concrete proof
that at least some short GRBs are produced by binary neutron star mergers. BurstCube will
provide localizations which will assist wide-field follow-up observers
in afterglow detection and redshift measurement. This will lead to
additional insight into cosmological parameter estimation, constraints
on the neutron star equation of state, and an inventory of r-process
elements in the Universe constrained by the faint short GRB kilonova
signature (seen in the most recent event). BurstCube will detect GRBs
(long and short) from the entire unocculted sky providing broadband
spectra for bursts detected elsewhere, rough localizations for
follow-up, and accurately timed light curves. BurstCube will also
detect solar flares, magnetar flares, and other hard X-ray transients,
as well as persistent sources via occultation analysis. In a future
without other GRB triggering instruments, BurstCube could provide
all-sky coverage for a small fraction of the cost of an Explorer.
Performance Stats:
Time Until Launch
NEWS AND INFORMATION
Here are some selected news items and publications.
Click on the images to make them bigger.


Cargo Cult Programming
During World War II, Japanese and American troops had to transport a great deal of cargo through the Pacific islands, setting up many short-lived landing strips along the way. Members of tribal societies in the area, who had not seen landing strips and planes before, didn’t quite understand the how or the why, only the fact that the presence of landing strips and planes coincided with the arrival of supplies. After the war, they would try to build immitation landing strips and planes in the hopes that this would cause new cargo to arrive.
This is an example of a cargo cult. The term has since been applied to a wide variety of other topics, when we supposedly-more-advanced people make the exact same mistake. When someone superficially mimics all the trappings of a legitimate scientist in order to support ideas with no scientific leg to stand on, they’re engaging in cargo cult science. Similarly, when programmers write unhelpful code because it looks or feels like something they ought to be doing, they’re engaging in cargo cult programming.
Years ago, I was working on a project in which the architects required the team to code the layers of our application according to a very strict pattern. It had all the trappings of a Good Layered Archictecture, but had the opposite effect in practice.
At the bottom, we had a Data Access Layer responsible for making database calls. Above that we had a Business Layer, which our ASP.NET code-behinds depended on. A general fear of exceptions led to a lot of rules about try/catch blocks and logging. It turned out, very little business was actually going on in these methods. They largely just forwarded arguments passed from the UI layer down to the Data Access Layer, amounting to hundreds of methods with the following structure:
The Handle method would serialize the exception to a log file, and then rethrow the exception after wrapping it in a BusinessException devoted to this layer.
Furthermore, each of these methods had to have full XML documentation in order to pass code review. Monotonous, you say? No worries, because everyone could install a plugin to automatically write those comments by inspecting the code.
In an elevator pitch, these might sound like good practices. “You don’t want your UI layer making database calls, right? You want to handle your exceptions, right? You want to log your exceptions, right? You need documentation, right?”
Unfortunately, these decisions were exceptionally costly in several ways. The initial costs came in development time. Imagine how often developers would reach for their copy and paste shortcuts while developing hundreds of these methods, effectively one for each database call. Imagine creating and then tracking down the inevitable copy/paste bugs. Imagine how silly I felt writing unit tests for these.
The documentation was almost always generated by a plugin, meaning that we all very quickly learned to skip over them while reading through the code. Whenever an important comment was actually written, it would therefore never been seen. A comment that tells me exactly what my code says is not a comment.
The costs were magnified when it came to troubleshooting production issues. Because every exception was caught, wrapped, and rethrown, stack traces became quite garbled and misleading. A similar try/catch appeared in the Data Access Layer, so when you tried to read the log file for a failed database call, the complexity was compounded. Instead of seeing a stack trace that showed the steps from the incoming request down to the actual point of failure, you had to walk through nested stack traces sprinkled with the untrue “failure” of each call to Handle().
The costs were further magnified in those cases where we actually did want some specific exception types to fall up to the caller. Here, in order to follow the pattern and pass code review, our try/catches had to get extra clever:
This all walked like exception handling, and quacked like exception handling, but it was really just a duck.
Handle()’s contributions were the logging of exceptions and the introduction of a layer-specific exception wrapper. Logging is useful, but could be performed by a logging framework higher up the call chain so that we don’t have to keep on remembering to include it. The layer-specific exception instance served no purpose other than to remind us that we did in fact have a layer named “Business”. Removing the exception handling pattern would leave us with the following:
Harrumph. This entire layer’s contribution, now, is to provide a namespace containing the word “Business”. It can be removed entirely. Hide the remaining Data Access layer behind some repository interfaces and you’re well on the way to an architecture that solves problems. To see where this train of thought eventually leads you, see the Onion Architecture.
Setting up conventions and patterns for your team to follow can be extrememly valuable, but those conventions and patterns always come with a cost. You have to weigh the cost of including a pattern with the savings they provide. You have to know what pain you’re solving. Even following a great pattern without understanding can lead to waste. You’ve got to trust your team members to learn and understand not only what to do, but why to do it.


Black Lives Matter Black Lives Matter, Black Lives Matter.


This past weekend (January 10th – 14th 2012) CodeMash was held in the Kalahari resort in Sandusky, OH. Yes I know – Ohio in January!! I had been warned that firstly its cold there and secondly there is usually a lot of snow. This time we were quite lucky and only got a little snow. I arrived on Wednesday night after a very long journey and walked into what was just a hive of activity. I have been to a lot of conferences, this one was not only huge but very interactive and social. A lot of the attendees brought their families for the week!
I was very lucky to have a session accepted for this conference (Introducing Continuous Delivery) and was extremely pleased to know that my session had made it from over 700 applications. I was also lucky enough to have my session early on Thursday. If you look at the schedule, marvel at the level of speakers here and you will see why I was so pleased!
The big thing for me on this conference is that there were a lot of companies there in force. Edgecase and Leandog are the 2 that stick out for me. Edgecase as they were usually in the hot tub drinking and leandog because of those hats . There was also the small matter of a party on Thursday evening after the sessions that actually moved to take over the waterpark between 10pm and 1am. A-Maze-ing! I, in fact, did ‘a Corey Haines’ and smashed my head off the bottom of the pool. At one point on the Friday, Cleveland were trying to get me to stand on the runway as a beacon for the planes to find the runway as it was so red!
a Corey Haines :
- To injure oneself at CodeMash in the waterpark
- OMG Dude you just did a Corey Haines – are you ok?
- Oh and the bacon bar – bacon with all sorts of toppings available – from butterscotch to white chocolate.
Without the sponsors this event would never have been able to happen. Please do say a thank you – tweet them even! I seen tweets as to the magnitude of the event:
Most importantly, I would sincerely like to thank all the organisers. These guys work on this conference while holding down jobs. Its an incredible accomplishment. It was what I would certainly class as the best conference ever. Thinking of 1300 geeks in a waterpark in Ohio in January doesn’t do this event justice at all. It is quite literally a software conference for the entire family.
I met so many cool people – I couldn’t begin to name them all or acknowledge them all! I will certainly be back next year!


This documentation is the developer documentation of Scelight, home page: https://sites.google.com/site/scelight/.
Module entry points:
- The Launcher entry point is {@link hu.sllauncher.ScelightLauncher}.
- The application entry point is {@link hu.scelight.Scelight}.
- External modules entry point is {@link hu.scelightapi.IExternalModule}.
You can find Scelight here: Scelight home page
@author Andras Belicza


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Our records show that your warranty is about to expire on the following equipment:
-
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We encourage you to schedule an inspection with us before the warranty expires if you haven’t already! You may also be able to add an extended warranty for your equipment. For more information, reach out to your account manager or call your local Hutson store at {{hutsonPhoneNumber}}.
Sincerely,
Hutson, Inc.
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I grew up with a heavy emphasis on having a "Renaissance education," which for my family was defined as being educated about many things, not just academic things. We were taught to try new things long enough to be comfortable with them, and then decide if they were what we wanted to focus on. Learning through play was emphasized; we later learned that it helped us learn new things well.
Growing up with these diverse experiences and perspectives, along with holding citizenship in more than one country, informed both my career and my way of looking at life. Again, I find myself working in a field that requires a mixture of skills: Analytical and creative; theoretical, academically-informed, and practical.
Curiosity, being adventurous, exploring and wanting to understand things all fuel my constant learning, practice, and growth. Finding joy in the unexpected just adds to the experience. This does not negate the role that practice lends to growth, as without practice, excellence cannot be achieved.


--- title: Scalability layout: page category: index description: 'A collection of posts related to scalability.' ---


Last build of the day. Ready to go home and road-trip to Bend tomorrow. Wa-hoo!
11/6/2008, 5:21:42 PM
Favs: 0
Retweets: 0
link--- layout: tweet ---
Last build of the day. Ready to go home and road-trip to Bend tomorrow. Wa-hoo!
11/6/2008, 5:21:42 PM
Favs: 0
Retweets: 0
link


14
A Plea to Return to the LORD
1 Return, O Israel, to the LORD your God,
for you have stumbled because of your iniquity.
2 Take with you words
and return to the LORD;
say to him,
“Take away all iniquity;
accept what is good,
and we will pay with bulls
the vows of our lips.
3 Assyria shall not save us;
we will not ride on horses;
and we will say no more, ‘Our God,’
to the work of our hands.

It is I who answer and look after you.
I am like an evergreen cypress;
from me comes your fruit.
9 Whoever is wise, let him understand these things;
whoever is discerning, let him know them;
for the ways of the LORD are right,
and the upright walk in them,
but transgressors stumble in them.


X Factor icon Ariel Burdett 'dies aged 38' as friends pay condolences online
X Factor star Ariel Burdett has reportedly died, according to new reports.
Metro.co.uk has contacted X Factor and friends for comment.
This story is being updated.
Got a showbiz story? If you've got a celebrity story, video or pictures get in touch with the Metro.co.uk entertainment team by emailing us<EMAIL_ADDRESS>calling 020 3615 2145 or by visiting our Submit Stuff page - we'd love to hear from you.
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Inter-sample condition variability is a key challenge of normalising ChIP-seq data. This implementation uses either spike-in or a second factor as a control for normalisation. Input can either be from 'DiffBind' or a matrix formatted for 'DESeq2'. The output is either a 'DiffBind' object or the default 'DESeq2' output. Either can then be processed as normal. Supporting manuscript Guertin, Markowetz and Holding (2017) <doi:10.1101/182261>.
| Version: | 1.0.8 |
| Depends: | R (≥ 2.10), DiffBind, Rsamtools, DESeq2, lattice, stats, utils, graphics |
| Published: | 2018-02-15 |
| Author: | Andrew N Holding |
| Maintainer: | Andrew N Holding <andrew.holding at cruk.cam.ac.uk> |
| License: | CC BY 4.0 |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | Brundle results |
| Reference manual: | Brundle.pdf |
| Package source: | Brundle_1.0.8.tar.gz |
| Windows binaries: | r-devel: Brundle_1.0.8.zip, r-release: Brundle_1.0.8.zip, r-oldrel: Brundle_1.0.8.zip |
| OS X binaries: | r-release: not available, r-oldrel: not available |
| Old sources: | Brundle archive |
Please use the canonical form https://CRAN.R-project.org/package=Brundle to link to this page.


{{ numberOfPlayers }}
active players
{{ numberAchieved }}
found treasures
Out of {{ numPlaying }} players with at least one award, here are people who have found more than one treasure.


Just replacing GET with POST sometimes isn't good enough. Make sure that the Content-Type header and other headers make sense for the data that you're posting:
Content-Type: application/x-www-form-urlencoded
Burp can right click in repeater to change request type
Don't forget about your good friend curl!
-X POST
-x http://<IP_ADDRESS>:8080 (for seeing your request in a proxy)
-d DATA TO POST
<target>
Changing to a post request:
Content-Type: multipart/form-data; boundary=--PleaseSubscribe
----PleaseSubscribe
Content-Disposition: form-data; name=*anything*; filename="LaveAComment"
Content-Type: text/plain
Please Share my videos
----PleaseSubscribe


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Being that this is my opportunity to create a résumé of my own design, I figured I'd take advantage of the creative freedom and show a bit of my personality in addition to the traditional skills and experience. If you would like to download a copy of my standard resume, here you go!
Background Story
The Road Not (Yet) Taken
An economics major I was
Staring at my first big decision;
I might follow the financial buzz
But I'd be doing that just because
I had still not yet found my vision.
Drew University
Bachelor's - Economics- Graduated cum laude.
- Studied in university's Semester on Wall Street program.
- Double-minor in business management and history.
I went into Drew having no idea what I wanted to do for a career. I came out with a deep appreciation for many fields, particularly the business world. At Drew, I participated in the university's "Semester on Wall Street" program and played one game of fall baseball before retiring with a career .333 batting average.
Considered sports agent life with stealth
But the legal side seemed like no fun;
My passion was not in building wealth
Rather helping those discover health;
Thus my road through wellness had begun.
New York Sports Clubs
Personal Trainer - Corrective Exercise Specialist- Created personalized training programs for client.
- Specialized in correcting postural distortions and faulty movement patterns.
- Sold personal training packages and assisted in gym membership sales.
My original crazy career plan was to bootstrap a sports agent practice. My idea was to begin by training amateur athletes and then representing them in their professional careers. Unfortunately, I wasn't enamored with the legal side of things and also decided it was much more rewarding to be able to work with people who needed help transitioning to a more healthy lifestyle. My specialization was in corrective exercise, in which I focused on correcting postural distortions and faulty movement patterns.
I found a road with no one on it
And invited others by my side;
Often we'd approach an open pit
And learn ourselves to build our transit
Yet the rewards came before the ride.
Fit Ignition LLC
Various Business and Consulting Projects- Designed and developed websites for multiple clients.
- Ran online promotional campaign for a dietitian's book launch (Reached Amazon top 100).
- Partnered in creation of online nutritional coaching program.
 It may not be apparent to the onlooker, but everything I've done to this point has been part of a single overarching theme. I left NYSC to pursue a project with a dietitian, which led to other projects with other dietitians and massage therapists. All of this was while maintaining a private training practice. These projects were part of my desire to reach more people than the few I could work with on a one-to-one basis. Along the way I helped promote a book launch, designed and built several websites, promoted private practices via diginal marketing, and created online coaching programs.
Twas here that I began to tinker
Web dev and programming self-study;
Pondering like Rodin's The Thinker
I needn't fall hook, line, and sinker
For beaten paths that seemed so muddy.
Self-Exploration
Web Development and Programming- Built own CMS from scratch for blog and website. (Django)
- Built app for tracking training client progress. (Reactjs, Node.js, Django)
- Current fun projects include building an autonomous robot.
I started my web development mini-career by building a few custom wordpress sites for myself, as well as several friends and business partners. I also spent time assisting a pro dev with some of his projects. After realizing I hated <? php ?>, I learned python and built my own CMS with django. I really wanted to build something to help with my training clients, so I began building an app to track their progress. Around this time, Reactjs became all the rage, so I spent quite a bit of time learning it before converting the front end of the app and my blog to be fully Reactified. I also learned the joys of deployment on Heroku and just enough about nginx to get the front and back ends playing nicely together. The experience gave me all sorts of crazy ideas, which ignited my desire to learn as much as possible about programming.
I'd often seek roads without access
In hopes of one day earning my pass;
The process surely would not lack stress
Others might call it a full-stack mess
While I calmly entered my next class
Self-Education
The Non-Traditional Education- Worked through many online courses in data science, programming, and mathematics.
- Try to read 2-3 research papers per week.
- Developed personal learning approach to deepen understanding from first principles to frontier.
For the past several years, there has not been a time in which I haven't been participating in at least one online course or educational program. I've gone through courses in physics, multivariable calculus, neuroscience, computer architecture, algorithms, machine learning, statistics & probability, multivariable calculus, linear algebra, data analysis, convolutional neural networks, probabilistic graphical models, and others. Part of this was pure curiosity, part was in preparating for a data science bootcamp, and part was an intense desire to do something extraordinarily meaningful with my life.
A road is but a road forever
A lonely path need not be single;
The bond of solitude would sever
And surround me with those more clever;
Wisdom expands when intermingled.
Metis
Data Science Bootcamp- 12 weeks of full-time immersive training in machine learning, statistical analysis, and programming.
- Built several end-to-end projects from data acquisition to modeling.
- Projects focused on gaining experience with a data pipeline while using real-world sources.
There are some people who will roll their eyes when they read about a data science bootcamp. But if you've read this far, I'm fairly confident you aren't one of them. I joined Metis to get some focused instruction and peer support to complement my self-education. To me, it was a natural—yet exciting—step on my journey. I didn't sleep much during those 12 weeks, as I was determined to fully immerse myself in the data science experience. The projects were great, but the people I met during (and after) the program are what I'll take with me everywhere I go. I learned, and continue to learn, so much from the diverse set of characters that I was surrounded by on a daily basis. Thank you all.
That leads me to the road not taken
Perhaps one which I have yet to find;
My journey is a lot to take in
But the road need not be forsaken;
Together may we explore in kind.
[Your Company Name Here]
Data Scientist - The Road Not (Yet) Taken- Looking for my next challenge.
- My emphasis is interesting work, having at least a little room to utilize my creativity, and growing my skills.
- Feel free to contact me any time.
This paragraph will one day be dedicated to the company that took a chance on a guy with an interesting background, a creative thought-process, an insatiable thirst for knowledge, and a strong desire to do meaningful work. No, I do not (yet) have a Master's or Ph.D. No, I do not have 8+ years of experience in whatever you might want 8+ years of experience. But I simply ask that you think about where you want your company/team/project to be a year from now. With that thought in mind, let's sit down and discuss how I just might be what's needed to get you there.
"Education is not the learning of facts, but the training of the mind to think."
-Albert Einstein
Skills
Below you will find a heatmap that represents a self-assessment of certain technical skills that may be of use in future roles. It is by no means a complete or scientific assessment, but rather an honest evaluation of my current comfort level.
- Python
- SQL
- Javascript(React, Node)
- R
- English
- Scikit-Learn
- Numpy
- Pandas
- Keras
- Scrapy
- Tensorflow
- Matplotlib
- OpenCV
- Geopandas
- Convolutional Neural Networks
- Linear Regression
- KMeans
- Random Forest
- Naive Bayes
- Recurrent Neural Networks
- Computer Vision
- Natural Language Processing
- Web Scraping
- GIS/Geospatial Analysis
- AWS/GCP
- Probabilistic Graphical Models
- Bash Scripting
- Strong
- Comfortable
- Capable
- Some Experience
Relevant Coursework
-
Data Science
- Metis Data Science Bootcamp (Link)
- Intro to Machine Learning (Coursera)
- Probabilistic Graphical Models (Coursera)
- Intro to Statistical Learning (Stanford Lagunita)
- Convolutional Neural Networks for Visual Recognition (Stanford Online Lectures/Assignments)
- Statistical Reasoning (Stanford Lagunita)
-
Mathematics
- Mathematics for Computer Science (MIT OCW)
- Probabilistic Systems Analysis and Applied Probability (MIT OCW)
- Intro to Linear Algebra (MIT OCW)
- Linear Algebra Foundations to Frontiers (edX)
- Introduction to Probability and Statistics (MIT OCW)
- Multivariable Calculus (MIT OCW)
-
Economics (Drew University)
- Econometrics
- Corporate Finance
- Wall Street and the Economy
- Micro/Macroeconomics
- Money and Banking
- Fundamentals of Financial Accounting


Football: Injured Egypt superstar Salah to miss Cup of Nations qualifiers
JOHANNESBURG: African superstar Mohamed Salah will not play for Egypt against Kenya on Thursday in 2021 Africa Cup of Nations qualifying because of a lingering ankle injury.
The 27-year-old forward was hurt against Leicester City last month and despite making several subsequent appearances for Liverpool, is suffering from slight discomfort in his left ankle.
Egyptian media reported that after Salah travelled from England to Mediterranean city Alexandria, national squad medical staff ruled him out of the Group G clash.
He will also miss the away fixture against the Comoros in Moroni next Monday.
Salah wore a protective boot on his left foot as he watched his Pharaohs teammates prepare in Alexandria to face the Harambee Stars at the 87,000-capacity Borg el Arab Stadium.
The absence of 2017 and 2018 African Footballer of the Year Salah is a major blow as Egypt rely heavily on him for goals.
Egypt have won the Cup of Nations a record seven times, twice more than Cameroon, but flopped as hosts of the 2019 tournament, losing to South Africa in the last 16.
Togo complete Group G and the top two finishers after a six-round mini-league qualify for the 24-nation finals in Cameroon.


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The first action you are creating is to load two pages with details from the main view. You want the action to act on each individual row. Inside the 'display_columns' element, add an additional 'col' element in 'action' type to display a trigger for each row. The trigger will then execute the action against its associated row.
The 'col' element attribute 'type' will have a value 'action' to indicate that this column is going to call an action. The attribute 'name' will be set to the same name as the action it is to call.
The action framework will be defined on this page. The element 'c_title' is set to the column title, and the element 'icon' is set to the file name of the icon to be displayed for the trigger.