Data Mining and Predictive Analytics
Posted on Wed 11 January 2017 in Data-Science
Data Mining and Predictive Analytics
-
Data Mining : The process of discovering useful patterns and trends in large data-sets.
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Predictive Analytics : The process of extracting information from large data-sets in order to make predictions and estimates about future outcomes.
What is Data Mining?
Example :
Problem : Dell was interested in improving the productivity of its sales workforce.
Analyze database of potential customers in order to identify most likely respondents
Machine Learning :
Statistical learning refers to a vast set of tools for understanding data.
These tools can be classified as
- Supervised : Supervised statistical learning involves building a statistical model for predicting, or estimating, an output based on one or more inputs.
- Unsupervised : There are inputs but no supervising output; nevertheless, we can learn relationships and structure from such data.
Supervised Learning :
When computers are applied to solve a practical problem it is usually the case that the method of deriving the required output from a set of inputs can be described explicitly.
The task of the system designer and eventually the programmer implementing the specifications will be to translate that method into a sequence of instructions which the computer will follow to achieve the desired effect.
Thus, when there is no set method (or no known method) for computing the desired output from a set of inputs, or where that computation may be very expensive.
Example : * Modelling a complex chemical reaction, where the precise interactions of the different reactants are not known, or
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Classification of protein types based on the DNA sequence from which they are generated, or
-
Classification of credit applications into those who will default and those who will repay.
These tasks cannot be solved by a traditional programming approach since the system designer cannot precisely specify the method by which the correct output can be computed from the input data.
An Alternate Strategy :
The computer can attempt to learn the input/output functionality from examples, in the same way that children learn which are sports cars simply by being told which of a large number of cars are sporty rather than by being given a precise specification of sportiness.
The approach of using examples to synthesize programs is known as the learning methodology, and in the particular case, when the examples are input/output pairs, it is called supervised learning. The examples of input/output functionality are referred to as the training data.
The input/output pairings typically reflect a functional relationship mapping inputs to outputs, though this is not always the case.
-
When the outputs are corrupted by noise.
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When an underlying function from input to output exists, it is referred to as the target function.
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The estimate of the target function which is learnt or output by the learning algorithm is known as the solution of the learning problem.
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In the case of classification, this function is sometimes referred to as the decision function.
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The solution is chosen from a set of candidate functions which map from the input space to the output domain.
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Usually, we will choose a particular set or class of candidate functions known as hypotheses before we begin trying to learn the correct function.
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For example, so-called decision trees are hypotheses created by constructing a binary tree with simple decision functions at the internal nodes and output values at the leaves.
- Hence, we can view the choice of set hypotheses (or hypotheses space) as one of the key ingredients of the learning strategy. The algorithm which takes the training data as input and selects a hypothesis from the hypothesis space is the second important ingredient. It is referred to as learning algorithm.
Classification:
Types of Classification : 1. Binary classification problem : Example : In the case of learning to distinguish sports cars the output is a simple Yes/No tag which we can think of as a binary output value. 2. Multi-Class Classification : Example : For the problem of recognising protein types, the output value will be one of a finite number of categories, While, the output values when modelling a chemical reaction might be the concentrations of the reactants given as real values.
Unsupervised Learning :
A case where there are no output values and the learning task is to gain some understanding of the process that generated the data. This type of learning includes density estimation, learning the support of a distribution, clustering and so on.There are even more complex models. The simplest case is when the learner is allowed to query the environment about the output associated with a particular input. The study of how this affects the learner's ability to learn different tasks is known as query learning. A more complex model of interaction is considered in reinforcement learning, where the learner has a range of actions at their disposal which they can take to attempt to move towards states, where they can expect high rewards. The learning methodology can play a part in reinforcement learning, if we treat the optimal action as the output of a function of the current state of the learner. There are, however, significant complications since the quality of the output can only be assessed indirectly as the consequences of an action become clear.
Basic Difference b/w Supervised and Unsupervised :
Example 1 : Face recognition
- Supervised learning : Learn by examples as to what a face is in terms of structure, color, etc so that after several iterations, it learns to define a face
- Unsupervised learning : Since there is no desired output in this case that is provided therefore categorization is done so that the algorithm differentiates correctly between the face of a horse, cat or human (clustering of data)
Thus, In supervised learning, the output datasets are provided which are used to train the machine and get the desired outputs whereas in unsupervised learning no datasets are provided, instead the given data is clustered into different classes.

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Scenario 2 : You go bag-packing to a new country, you did not know much about it - their food, culture, language etc. However from day 1, you start making sense there, learning to eat new cuisines, including what not to eat, find a way to that beach etc.
Example 3 :
Supervised Learning:
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Problem : Given a basket of fruits, arrange same types of fruits in one place.
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Previous Work : The shape of each and every fruit is known, so it is easy to arrange the same type of fruits at one place.
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Suppose fruits are : apple,banana,cherry,grape.
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With the previous work, the shape of each and every fruit so, it is easy to arrange the same type of fruits at one place.
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Here, the previous work is called as train data in data mining.
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There's a response variable which tells where the fruit needs to be placed, based on the features of the fruit.
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This type of data is called the train data.
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This type of learning is called supervised learning.
Unsupervised Learning:
-
Problem : Given a fruit basket. Arrange the same type of fruits at one place.
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But, this time, nothing is given (i.e. no previous work). What will be the category of classification?
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The category would be the physical character of the particular fruit.
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For example : "Fruit Color"
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Thus, no training data and no response variable.
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i.e. Unsupervised Learning.
Applications :
The availability of reliable learning systems is of strategic importance, as there are many tasks that cannot be solved by classical programming techniques, since no mathematical model of the problem is available.
So for example it is not known how to write a computer program to perform hand-written character recognition, though there are plenty of examples available.
It is therefore natural to ask if a computer could be trained to recognize the letter ?A' from examples - after all this is the way humans learn to read. We will refer to this approach to problem solving as the learning methodology The same reasoning applies to the problem of finding genes in a DNA sequence, filtering email, detecting or recognizing objects in machine vision, and so on.
Dataset Examples :
Wage dataset
- In this application, we examine a number of factors that relate to wages for a group of males from the Atlantic region of the United States. In particular, we wish to understand the association between an employee's age and education, as well as the calendar year on his wage.
Analysis :
- Wage increases with age.
- Wage decreases after 60.
- There is a slow but steady increase of approximately \(\\)10,000$ in the average wage between 2003 to 2009.
- Higher the education, better the wage.
Thus, given an employee's age, we can use this curve to predict his wage. However, it is also clear, that there is a significant amount of variability associated with this average value. Thus, age alone is unlikely to provide an accurate prediction of a particular man's age.
We also have information regarding each employee's education level and the year in which the wage was earned.
Wages increase by approximately \(10,000\), in a roughly linear (or straight-line) fashion, between 2003 - 09. Though this rise is very slightly relative to the variability in the data. Wages are also typically greater for individuals with higher education levels.
SVM :
Support Vector Classification :
The aim of Support Vector classification is to devise a computationally efficient way of learning ?good? separating hyperplanes in a high dimensional feature space. Here, : By 'good' hyperplanes we consider the ones optimising the generalisation bounds, and By 'computationally efficient' we mean algorithms able to deal with sample sizes of the order of 100 000 instances.
Pre-requisites : * The generalisation theory gives clear guidance about how to control capacity and hence prevent overfitting by controlling the hyperplane margin measures, while
- Optimisation theory provides the mathematical techniques necessary to find hyperplanes optimising these measures.
Different generalisation bounds exist, motivating different algorithms: one can for example optimise the maximal margin, the margin distribution, the number of support vectors, etc.
Focus : * Reduce the problem to minimising the norm of the weight vector.


A Ocamlyacc grammar rule has the following general form:
result: symbol ... symbol { semantic-action } | ... | symbol ... symbol { semantic-action } ;
where result is the nonterminal symbol that this rule describes, and symbol are various terminal and nonterminal symbols that are put together by this rule (see Symbols).
For example,
exp: exp PLUS exp {} ;
says that two groupings of type exp, with a PLUS token in between, can be combined into a larger grouping of type exp.
Whitespace in rules is significant only to separate symbols. You can add extra whitespace as you wish.
At the end of the components there must be one action that determine the semantics of the rule. An action looks like this:
{Ocaml code}
See Actions for detail description.
Multiple rules for the same result can be written separately or can be joined with the vertical-bar character | as follows:
result: rule1-symbol ... rule1-symbol { rule1-semantic-action } | rule2-symbol ... rule2-symbol { rule2-semantic-action } | ... ;
They are still considered distinct rules even when joined in this way.
If components in a rule is empty, it means that result can match the empty string. For example, here is how to define a comma-separated sequence of zero or more exp groupings:
expseq: /* empty */ {} | expseq1 {} ; expseq1: exp {} | expseq1 COMMA exp {} ;
It is customary to write a comment /* empty */ in each rule with no components.


Gateway
BOT
14-Aug-18 06:24 AM
[dscrdeql] <CombatWombat-Mod> should of waited one week
[dscrdeql] <CombatWombat-Mod> but i got some more EQL
[dscrdeql] <CombatWombat-Mod> silly person still dumping how stupid
[dscrdeql] <NWhiskeyTango> ya silly
[dscrdeql] <edsky> I feeling ya on kmd @CombatWombat-Mod
[dscrdeql] <x2maano>
[dscrdeql] <CombatWombat-Mod> i know right edsky
[dscrdeql] <edsky> Deleted my cmc bookmark days ago
[dscrdeql] <CombatWombat-Mod> we can't catch the bottom lol
[dscrdeql] <CombatWombat-Mod> so much cheap things to buy but they might even go lower
[dscrdeql] <NWhiskeyTango> im afraid you might be right
[dscrdeql] <CombatWombat-Mod> if btc doesn't hold we will see more blood lol
[dscrdeql] <edsky> Yeah who knows whats going on better go fishing and take your mind off it
[dscrdeql] <CombatWombat-Mod> haha fishing sounds good
[dscrdeql] <NWhiskeyTango> i been working a lot, i'd rather be fishing too
[dscrdeql] <edsky> Caught a 13 pound red emperor on sunday
[dscrdeql] <edsky> I’d post the pic but I can’t
[dscrdeql] <CombatWombat-Mod> cant post in another channel?
[dscrdeql] <blademaster> whos getting busy!
[dscrdeql] <CombatWombat-Mod> i will later gotta go airport soon
[dscrdeql] <blademaster> oh airport? going on holidays?
[dscrdeql] <CombatWombat-Mod> not me my mother is going overseas
[dscrdeql] <blademaster> ah lucky her
[dscrdeql] <CombatWombat-Mod> im staying here no holiday for me
[dscrdeql] <blademaster> i need a holidays myself
[dscrdeql] <NWhiskeyTango> bummer
[dscrdeql] <CombatWombat-Mod> yeah she going to visit her mum and brothers
[dscrdeql] <CombatWombat-Mod>
[dscrdeql] <blademaster> welcome to the stay at home group... or club rather
(edited)
[dscrdeql] <CombatWombat-Mod> i really want to put more fiat into cryptos right now as everything is so cheap
[dscrdeql] <blademaster> nice. family gathering i see
[dscrdeql] <CombatWombat-Mod> but not sure if i can afford too lol
[dscrdeql] <AndronicusBass-Founder> Ahh, the airport. often good food places in the food court
[dscrdeql] <blademaster> problem is. it is on a downtrend so it could be lower , who knows
[dscrdeql] <CombatWombat-Mod> yeah im gonna get hungry jacks bass
[dscrdeql] <CombatWombat-Mod> for some reason the melb airport hungry jacks is the best
[dscrdeql] <CombatWombat-Mod>
[dscrdeql] <blademaster> its heading towards december and thats usually where i goes down really low and head straight up!
[dscrdeql] <AndronicusBass-Founder> whopper with cheese what you normally get?
[dscrdeql] <blademaster> i just had hungry jacks todayhaha
[dscrdeql] <CombatWombat-Mod> yeah whopper with cheese extra sauce
[dscrdeql] <blademaster> loaded chips for me
(edited)
[dscrdeql] <AndronicusBass-Founder> hey you know you can show the vouchers on your phone screen and still get the discounts
[dscrdeql] <NWhiskeyTango> word
[dscrdeql] <CombatWombat-Mod> see even tipsy bot knows thats the good stuff
[dscrdeql] <AndronicusBass-Founder> haha, tipsy knows tasty goodness
[dscrdeql] <CombatWombat-Mod> @AndronicusBass-Founder yep lol i always use them
[dscrdeql] <blademaster> the shake the phone voucher?
[dscrdeql] <CombatWombat-Mod> nah the vouchers
[dscrdeql] <CombatWombat-Mod> remember the one's you used to get sent in the mail
[dscrdeql] <CombatWombat-Mod> you just download them on your phone now
[dscrdeql] <CombatWombat-Mod> its like half price with the vouchers
[dscrdeql] <blademaster> i prefer to shake the phone lol instant reponse
[dscrdeql] <blademaster> on selected stores
[dscrdeql] <blademaster> hungry jacks gotten better lately. was there a new boss?
[dscrdeql] <AndronicusBass-Founder> nice, I can eat 2 whoppers with cheese 2 small fries and 2 small sprite in one sitting.
[dscrdeql] <AndronicusBass-Founder> Im making chips with cheese and gravy at the moment
[dscrdeql] <blademaster> frozen mango
[dscrdeql] <CombatWombat-Mod> yea i can too but i prefer eating that at home so i can pass out on the couch after
[dscrdeql] <CombatWombat-Mod> hahaha
[dscrdeql] <blademaster> hungry jacks and beers ahhhhh
[dscrdeql] <CombatWombat-Mod>
[dscrdeql] <akomismo> No command for avatar?
[dscrdeql] <AndronicusBass-Founder> try adding cheese to the tender crisp chicken classic 2 for $7.95 cheese is extra, its discusting chemical crap but so tasty haha
[dscrdeql] <akomismo> Wow can you tip me 1 pc of crispy chicken?
[dscrdeql] <edsky> Have two mate
[dscrdeql] <blademaster> i feel like chicken tonight!
(edited)
[dscrdeql] <edsky> I prefer steak!
[dscrdeql] <x2maano> honey
[dscrdeql] <AndronicusBass-Founder> LOL https://youtu.be/Fl3GC6bxmWE
The Late Show classic skit featuring Rob Sitch and Jane Kennedy as the parents as well as Nick A and Mel B as the kids. Also starring Mick Molloy, Tony Marti...
[dscrdeql] <Jonathan-Mod> Lol nice
[dscrdeql] <CombatWombat-Mod> waiting for my withdrawal from exrates... I have never withdrawn at this time
[dscrdeql] <CombatWombat-Mod> hopefully its not a big wait
[dscrdeql] <CombatWombat-Mod> i still hate the waiting game
[dscrdeql] <CombatWombat-Mod> even though I have had to wait so many times
[dscrdeql] <CombatWombat-Mod> i feel better once i see the balance in my wallet
[dscrdeql] <CombatWombat-Mod> lol
[dscrdeql] <Jonathan-Mod> Last one was pretty quick for me
[dscrdeql] <CombatWombat-Mod>
[dscrdeql] <AndronicusBass-Founder> seems to vary in time from what ive heard.
[dscrdeql] <NWhiskeyTango> my one withdrwal was over the weekend
[dscrdeql] <Jonathan-Mod> :linktriforce:
[dscrdeql] <CombatWombat-Mod> yep i normally withdraw much later but some idiot dumped eql again and my order got filled
[dscrdeql] <CombatWombat-Mod> idiots filling up my bag lol
[dscrdeql] <NWhiskeyTango> man i wish i could buy more
[dscrdeql] <CombatWombat-Mod> i was not supposed to buy more but i dunno what happened
[dscrdeql] <CombatWombat-Mod> lol
[dscrdeql] <CombatWombat-Mod> ahhhh ive already spent 25 percent of the BTC i was supposed to hold and cash out
[dscrdeql] <CombatWombat-Mod> hahaha
[dscrdeql] <Jonathan-Mod> I do everytime I get paid lol
[dscrdeql] <CombatWombat-Mod> nah i have not put fiat into cryptos for a while
[dscrdeql] <CombatWombat-Mod> but i made some good profits of a poop coin and sold for btc
[dscrdeql] <CombatWombat-Mod> the plan was to cash that out and take back my initial investment
[dscrdeql] <CombatWombat-Mod> that plan has not worked well so far
[dscrdeql] <CombatWombat-Mod>
[dscrdeql] <Jonathan-Mod> I don't get paid in fiat


EBD - Easton Bible DIctionary
Baal-hanan
BAAL-HANAN
"BAAL-HANAN" Lord of grace. (1.) A king of Edom, son of Achbor(
Genesis 36:38, 39
;
1 Chronicles 1:49, 50
).
(2.) An overseer of “the olive trees and sycomore trees in the low plains”(the Shephelah) under David (
1 Chronicles 27:28
).
"BAAL-HANAN" Lord of grace. (1.)  An overseer of “the olive trees and sycomore trees in the low plains”(the Shephelah) under David (1 Chronicles 27:28).


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GCDSS Bajoga Computer Based Test
Madaki Fatsen
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BUILDING YOUR BRAND IS SIMPLE
business, lifestyle
This is a very simple example of building sliders with LayerSlider. While it uses only a few layers with minimalistic transitions, a content slider like this can be attractive at the right place.


Thank you for being a friend. Travelled down the road and back again.  It's time to put on makeup. It's time to dress up right.
They're all together ooky the Addams Family doin' it our way. There's nothing we wont try. Never heard the word impossible. This time there's no stopping us." They're creepy and they're kooky mysterious and spooky all together ooky.


6.9 psb_spasb — Sparse matrix assembly routine
call psb_spasb(a, desc_a, info [, afmt, upd, mold])
-
Type:
- Synchronous.
-
On Entry
-
-
desc_a
- the communication descriptor.
Scope:local.
Type:required.
Intent: in/out.
Specified as: a structured data of type psb_desc_type.
-
afmt
- the storage format for the sparse matrix.
Scope: local.
Type: optional.
Intent: in.
Specified as: an array of characters. Defalt: ’CSR’.
-
upd
- Provide for updates to the matrix coefficients.
Scope: global.
Type: optional.
Intent: in.
Specified as: integer, possible values: psb_upd_srch_, psb_upd_perm_
-
mold
- The desired dynamic type for the internal matrix storage.
Scope: local.
Type: optional.
Intent: in.
Specified as: an object of a class derived from psb_T_base_sparse_mat.
-
On Return
-
-
a
- the matrix to be assembled.
Scope:local
Type:required
Intent: inout.
Specified as: a structured data of type psb_Tspmat_type.
-
desc_a
- the communication descriptor.
Scope:local.
Type:required.
Intent: in/out.
Specified as: a structured data of type psb_desc_type. If the matrix was
allocated with bldmode=psb_matbld_remote_, then the descriptor will be
reassembled.
-
info
- Error code.
Scope: local
Type: required
Intent: out.
An integer value; 0 means no error has been detected.
Notes
- On entry to this routine the descriptor must be in the assembled state,
i.e. psb_cdasb must already have been called.
- The sparse matrix may be in either the build or update state;
- Duplicate entries are detected and handled in both build and update state,
with the exception of the error action that is only taken in the build state,
i.e. on the first assembly;
- If the update choice is psb_upd_perm_, then subsequent calls to psb_spins
to update the matrix must be arranged in such a way as to produce exactly
the same sequence of coefficient values as encountered at the first assembly;
- The output storage format need not be the same on all processes;
- On exit from this routine the matrix is in the assembled state, and thus
is suitable for the computational routines;
- If the bldmode=psb_matbld_remote_ value was specified at allocation
time, contributions defined on the current process but belonging to a
remote process will be handled accordingly. This is most likely to occur in
finite element applications, with dupl=psb_dupl_add_; it is necessary to
check for possible updates needed in the descriptor, hence there will be a
runtime overhead.


{% extends "base.html" %} {% block title %}{{ app_name }} - About{% endblock title %} {% block content %}
{{ app_name }} is a repository for population scale phenotype data for {{ app_prefix }} ({{ app_species }}) species.
Inspired by and based on AraPheno, for the model plant Arabidopsis thaliana.


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The Timing Framework is a set of utility classes that enables you to create powerful animations in your Java applications.
_______ _ /_ __(_)____ ___ (_)____ ____ _ / / / // __ `__ \/ // __ \/ __ `/ / / / // / / / / / // / / / /_/ / /_/ /_//_/ /_/ /_/_//_/ /_/\__, / /____/ ______ __ / ____/_________ _____ ___ ___ _ ______ _____/ /__ / /_ / ___/ __ `/ __ `__ \/ _ \ | /| / / __ \/ ___/ //_/ / __/ / / / /_/ / / / / / / __/ |/ |/ / /_/ / / / ,< /_/ /_/ \__,_/_/ /_/ /_/\___/|__/|__/\____/_/ /_/|_|
The Timing Framework was implemented by Chet Haase and introduced in the book Filthy Rich Clients (Haase and Guy, Addison-Wesley, 2008).
This "classic" version of the Timing Framework has the API described Filthy Rich Clients. It is not actively maintained and is only provided only as a historical curio for folks who are trying out the examples in the book.
DO NOT USE THIS VERSION FOR NEW DEVELOPMENT!
Why? Newer releases of the library solve a problem with the Swing timer that was discussed in Chet's JavaOne 2008 talk, are multi-thread safe, go faster, use less memory, support both Swing and SWT, and have an improved API.
The original Timing Framework implementation was released under the following terms:
Copyright (c) 2007, Sun Microsystems, Inc All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the Timing Framework project nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
The enhancements to the original implementation are released under the Apache License, Version 2.0.


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Course Offering | ||||
| Course Number | Mode of Instruction | Days | Time | Location |
|---|---|---|---|---|
| CS 101 | Face to face | Mon Wed | 6:00 PM - 8:00 PM | Bellevue |
This course introduces the essential technologies, testing concepts, and techniques that are associated with browser-based applications. The students will be introduced to a foundation of Web system architecture and technologies; they will get knowledge of Web-specific concepts, testing practices and methodologies, website design principles and practices. This course is intended for beginner or intermediate testers/developers who want to develop a working knowledge of Web software testing methodology.
Notepad, Notepad++
No prerequisites
There are no required textbooks for this course. You can go to online use open materials that talk about programming using C#.
Assessments | ||
| Component | Weight | Date |
|---|---|---|
| Assignments | 30% | TBD |
| Projects | 30% | TBD |
| Final Exam | 40% | TBD |


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{% load static %}
In the quest for answering this question we are gathering data scientists' expertise and knowledge, for building a model able to help "citizen data scientists" to use data platforms better.
In this survey you will be asked to infer data science task(s) carried out using imported packages in python scripts . Following an example of a task :
If a patient undergoes Bone Density Study, then the medical task
| is NOT Bone Density Study | is NOT X-ray | is Diagnosis * |
|---|
* As you have noticed a task is the more general term.
Privacy Policy
We confirm that your e-mail address will be stored in an encrypted form, will not be published or
linked to the survey results, will not be used for any further communication, and will not be shared with
third parties.
If you are interested in this survey results you can use your e-mail address as ID to
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back and check the results when it is published.
An expert survey on tasks of data science, Can we automate data science?
In the quest for answering this question we are gathering data scientists' expertise and knowledge, for building a model able to help "citizen data scientists" to use data platforms better.
To thank you for participating in our survey, we will be giving a 20 euro amazon voucher to 5 randomly selected participants.To enter the draw you need to finish the survey and submit this form. We will select the 5 lucky winners as soons as we have reached 50 participants. Also, if you have any comments on the survey, please include them here. Thanks and good luck.


PaintIt! community has the members around the world.Learn painting from the experts from your favorite field of paiting.Meet our experts from different fields of painting!
Classic Art Expert
Mia Morris
This is Mia Morris from Amsterdam.Loves to paint Arctitrcture paintings.


nips nips2003 nips2003-76 nips2003-76-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Anton Schwaighofer, Marian Grigoras, Volker Tresp, Clemens Hoffmann
Abstract: In this article, we present a novel approach to solving the localization problem in cellular networks. The goal is to estimate a mobile user’s position, based on measurements of the signal strengths received from network base stations. Our solution works by building Gaussian process models for the distribution of signal strengths, as obtained in a series of calibration measurements. In the localization stage, the user’s position can be estimated by maximizing the likelihood of received signal strengths with respect to the position. We investigate the accuracy of the proposed approach on data obtained within a large indoor cellular network. 1
[1] Bahl, P., Padmanabhan, V. N., and Balachandran, A. A software system for locating mobile users: Design, evaluation, and lessons, 2000. Revised version of Microsoft Research Technical Report MSR-TR-2000-12, available from the authors webpages.
[2] Castro, P., Chiu, P., Kremenek, T., and Muntz, R. A probabilistic room location service for wireless network environments. In Proceedings of the 3rd International Conference on Ubiquitous Computing (Ubicomp 2001). 2001.
[3] Hamprecht, F. A. and Agrell, E. Exploring a space of materials: Spatial sampling design and subset selection. In J. N. Cawse, ed., Experimental Design for Combinatorial and High Throughput Materials Development. John Wiley & Sons, 2002.
[4] Hashemi, H. The indoor radio propagation channel. Proceedings of the IEEE, 81(7):943–968, 1993.
[5] Ladd, A. M., Bekris, K. E., Rudys, A., Marceau, G., Kavraki, L. E., and Wallach, D. S. Roboticsbased location sensing using wireless ethernet. In Proceedings of the Eight ACM International Conference on Mobile Computing and Networking (MOBICOM 2002). 2002.
[6] Rasmussen, C. E. Evaluation of Gaussian Processes and other methods for non-linear regression. Ph.D. thesis, University of Toronto, 1996.
[7] Roos, T., Myllym¨ ki, P., Tirri, H., Misikangas, P., and Siev¨ nen, J. A probabilistic approach a a to WLAN user location estimation. International Journal of Wireless Information Networks, 9(3):155–164, 2002.
[8] Schwaighofer, A. Kernel Systems for Regression and Graphical Modelling. Ph.D. thesis, Institute for Theoretical Computer Science, Graz University of Technology, Austria, 2003.
[9] Stein, M. Interpolation of Spatial Data. Some Theory for Kriging. Springer Verlag, 1999.


Preparation time
overnight
Cooking time
1 to 2 hours
Serves
Serves 4
Dietary
Take your pavlova to the next level with some cheffy tricks from John Torode.
Preheat the oven for 135C/115C Fan/Gas 1. Line a large baking tray with baking paper.
Dissolve the vanilla essence, lemon essence and vinegar in the hot water.
Using an electric mixer, whisk the egg whites until stiff in a very clean bowl. Fold in the sugar and cornflour and continue to whisk for 2 minutes, then add the water mixture and beat until stiff peaks form.
Using a very large kitchen spoon make 12 large quenelles from the meringue mixture and place on the lined tray. Cook the meringue for 1 hour, then turn the oven off and leave the meringue in the oven overnight.
Meanwhile, for the lemon curd. Whisk the yolks and sugar until smooth and pale. Stir in the lemon and lime juice. Put the mixture through a sieve into a heatproof bowl. Add the butter. Put the bowl over a pan of gently simmering water and cook the curd until smooth and thickened.
For the candied peel. Bring a small saucepan of water to the boil, add the rinds and cook for 20 seconds. Strain and boil again in fresh water. For the third boil, add the sugar to 100ml/3½fl oz water and heat gently until dissolved. Return the rind to the pan. Once the liquid has reduced to a syrup remove from the heat. Carefully remove the rind (boiling sugar gets very hot) and set aside to cool.
Mix the curd with the whipped cream. Top the pavlovas with the whipped cream mixture and some candied peel. Serve 3 mini pavlovas per person.


You stomp closer to the new librarians. Then you pause to look back at the two by the stairs, very dramatically.
Holding up the key and using your Lord of the Rings voice, you ask the new librarians if they see the key while you hold it up like Simba in the Lion King. The librarians look at you for a whole minute without saying anything. They look at each other, then one looks at you and asks, "So, is this a magic trick? Are you going to pull a key out of thin air now?"
 Ha, ha, No! It's a joke! Apparently not a good one, though, ha... You make a 360 turn looking at all the librarians surrounding you. Tough crowd, you say. The drive-thru librarians look at you, smiling politely.
Don't mind me, you tell them. Im just looking around. A customer drives up and distracts one librarian as the other goes back to working on the computer.
| Go North | ||
| . . . . . | Go East | |
| Go South |


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coronavirus (), COVID- RNA SARS-CoV nsp spike {border-style Groot protein px;} #ffffff;} coronaviruses novel viral Chen respiratory recombination host (Kumar Coronavirus human proteins syndrome glycoprotein viruses (); Human SARS SARS-CoV- acute genome reported structural (COVID-) emergence glycosylation sequence transmission virus Analytics Clarivate Kumar Types antigenic difference emerged px; similarity {font-family COVID-. Lim disease epidemic font-size https://www.cdc.gov/coronavirus/types.html; nonstructural sites species (Cui Cui RBD Virus bats double; minimal severe Betacoronavirus SARS-CoV. border-color border-width cellular domain glycoproteins receptor receptor-binding related solid; treatment variation {font-style {font-weight (Lau ACE Chan China Coronaviridae Disease East Holmes Phan SARS-like Severe Spike Table accessory acid analysis attachment available barrier bat bold courier; development em; envelope epitopes family gene mechanism new number pandemic present pt;} relationship revealed similar ssRNA understand vaccine viruses. â€"


Who is going ?
Trip to, Ubud bali Indonesia
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Local area hospital to be upgraded to 250-bed facility
People of Narasampet constituency celebrate
by Special CorrespondentPeople of Narasampet constituency hailed the decision of the State government to upgrade the local area hospital to 250-bed hospital, which was their long-pending demand.
The people of the constituency thanked MLA Peddi Sudharshan Reddy for his efforts.
Mr. Reddy on his part thanked the government for sanctioning ₹ 60 crore for the development of the hospital which would be converted into a district hospital for the benefit of the people of Narsampet and surrounding mandals in Warangal rural district, which are predominantly tribal areas.
“Following my efforts, a dialysis centre was already set up at the hospital for the benefit of kidney patients. Besides this, a blood bank was also set up at the area hospital recently,” he said, and added that the 250-bed hospital would come up on five acres of land on the outskirts of the city with all modern facilities and equipment.
The development of Narsampet hospital will reduce the burden on the Mahatma Gandhi Memorial Hospital, Warangal, he said.
Meanwhile, TRS town president Naini Narsaiah and party mandal president Namala Satyanarayana, Lekkala Vidyasagar Reddy along with party workers and people offered palabhisekham to the portrait of Chief Minister K Chandrashekar Rao and MLA Peddi Sudharshan Reddy in Narsampet on Monday.
They also celebrated the occasion by distributing sweets. In Chennaraopet mandal too, TRS leaders, including party mandal president Balne Venkanna, mandal parishad vice-chairman Kandi Krishna Reddy and others burst crackers and distributed sweets.


Scream 2 (1997)
Director: Wes Craven Cast: Neve Campbell, David Arquette, Courtney Cox, Jamie Kennedy, Laurie Metcalf, Jerry O'Connel Rated R: Strong violence, gore, language
by Nathaniel R. Atcheson<EMAIL_ADDRESS>Is there a point at which a film can become so absurd that you simply stop watching it? I almost got to that point during the climax of Scream 2, which is one of the silliest, loudest, most obnoxious finales to a film I've seen in a good long while. The film itself is an exercise in self-awareness, where every cliché and every shot of someone being pounced by a knife-wielding maniac is observed with a tongue-in-cheek attitude. It seems almost pointless to even talk about the film, for anything that I might decide to criticize it for is probably an intentional element added by director Wes Craven and writer Kevin Williamson not out of ignorance, but to keep the tone consistent.
Okay, so the story is kind of weak, in that Scream sort of way, but it doesn't matter because the characters all talk about how sequels are "by definition," lesser films than the originals.  So some psycho goes around killing innocent college students in an attempt to upset and later destroy Neve Campbell. "Someone has taken his love for sequels one step too far!" shouts the preview. I think that's an accurate assessment of the film.
After some initial setup-gore, the characters from the original film--Gail Weathers (Courtney Cox), Dewey Riley (David Arquette), and Randy the movie nerd guy (Jamie Kennedy)--talk about who the killer might me. They discuss at great lengths the ideas behind cliché characters, about how the boyfriend isn't the killer because it's already been done, and so forth. All the gore aside, this film is so funny. I laughed frequently, and I'm not talking about chuckles--it was like "HA HA HA!" funny.
A film like Scream 2 can be successful for no other reason than it's almost sickeningly complete tone.  There's no question about it. It's going to happen. But here we have a problem--the movie is so funny, so unrealistic, that it actually one-ups its predecessor as far as laughs an concept goes. In the process, the gore and violence feels out of place and often forced.
Scream had some genuinely frightening moments in it--the opening scene, in particular, was tense and horrifying because we didn't know what was going on. There isn't a single startling moment in Scream 2, not one ounce of suspense, and the numerous shots of a knife plunging deep into the backs of college students is really intrusive. Scream's attitude was like, "Woah, this is like one of those horror flicks or something." Scream 2 feels more like, "Check it out, we're in another horror movie, and that's not possible, but isn't this wild?" I think it would have been even better if they thought there was a killer, but it was something else (strange forms of suicide or something like that) so they all turn out to be wrong, but they can talk about sequels anyway. Of course, that would be even sillier than the film already is.
As is, the horror is an excuse for the comedy, and though it works for the most part, elements feel out of place. The last twenty minutes, for example, deliver an unsatisfying conclusion to the point of me rolling my eyes and giving up on the film. Then, the heroine does some stuff (I'll keep it a surprise) that really rubbed me the wrong way, and I was left with a bad taste in my mouth. It looses its self-awareness and becomes absurd out of carelessness. And that's problematic. But reflecting back on the pre-conclusion part of the film, it's really funny. For what it is, Scream 2 works. And if you like to laugh, then you'll be doing some of that during the course of this film.
>From 0-10: 7 Grade: B-
Visit FILM PSYCHOSIS at http://www.pyramid.net/natesmovies
Nathaniel R. Atcheson
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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review and has no editorial control. Unless stated otherwise, the copyright
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Good Quality, only 2 time used
1 stock
Smooth stretch fabric, contrast binding, round neckline, cap sleeves, ruched side detail.
Take your shoe style to new heights with this alluring peep toe court shoe. Features a slim high heel and metallic detailing along the platform. Team with a high waisted pencil skirt and midi top for after dark glam. Take your shoe style to new heights with this alluring peep toe court shoe. Features a slim high heel and metallic detailing along the platform.
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Color : Black, Red, White
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| 1 | /* =========================================================== | |
| 2 | * JFreeChart : a free chart library for the Java(tm) platform | |
| 3 | * =========================================================== | |
| 4 | * | |
| 5 | * (C) Copyright 2000-2009, by Object Refinery Limited and Contributors. | |
| 6 | * | |
| 7 | * Project Info: http://www.jfree.org/jfreechart/index.html | |
| 8 | * | |
| 9 | * This library is free software; you can redistribute it and/or modify it | |
| 10 | * under the terms of the GNU Lesser General Public License as published by | |
| 11 | * the Free Software Foundation; either version 2.1 of the License, or | |
| 12 | * (at your option) any later version. | |
| 13 | * | |
| 14 | * This library is distributed in the hope that it will be useful, but | |
| 15 | * WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY | |
| 16 | * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public | |
| 17 | * License for more details. | |
| 18 | * | |
| 19 | * You should have received a copy of the GNU Lesser General Public | |
| 20 | * License along with this library; if not, write to the Free Software | |
| 21 | * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, | |
| 22 | * USA. | |
| 23 | * | |
| 24 | * [Java is a trademark or registered trademark of Sun Microsystems, Inc. | |
| 25 | * in the United States and other countries.] | |
| 26 | * | |
| 27 | * ------------- | |
| 28 | * RingPlot.java | |
| 29 | * ------------- | |
| 30 | * (C) Copyright 2004-2009, by Object Refinery Limited. | |
| 31 | * | |
| 32 | * Original Author: David Gilbert (for Object Refinery Limtied); | |
| 33 | * Contributor(s): Christoph Beck (bug 2121818); | |
| 34 | * | |
| 35 | * Changes | |
| 36 | * ------- | |
| 37 | * 08-Nov-2004 : Version 1 (DG); | |
| 38 | * 22-Feb-2005 : Renamed DonutPlot --> RingPlot (DG); | |
| 39 | * 06-Jun-2005 : Added default constructor and fixed equals() method to handle | |
| 40 | * GradientPaint (DG); | |
| 41 | * ------------- JFREECHART 1.0.x --------------------------------------------- | |
| 42 | * 20-Dec-2005 : Fixed problem with entity shape (bug 1386328) (DG); | |
| 43 | * 27-Sep-2006 : Updated drawItem() method for new lookup methods (DG); | |
| 44 | * 12-Oct-2006 : Added configurable section depth (DG); | |
| 45 | * 14-Feb-2007 : Added notification in setSectionDepth() method (DG); | |
| 46 | * 21-Jun-2007 : Removed JCommon dependencies (DG); | |
| 47 | * 23-Sep-2008 : Fix for bug 2121818 by Christoph Beck (DG); | |
| 48 | * | |
| 49 | */ | |
| 50 | ||
| 51 | package org.jfree.chart.plot; | |
| 52 | ||
| 53 | import java.awt.BasicStroke; | |
| 54 | import java.awt.Color; | |
| 55 | import java.awt.Graphics2D; | |
| 56 | import java.awt.Paint; | |
| 57 | import java.awt.Shape; | |
| 58 | import java.awt.Stroke; | |
| 59 | import java.awt.geom.Arc2D; | |
| 60 | import java.awt.geom.GeneralPath; | |
| 61 | import java.awt.geom.Line2D; | |
| 62 | import java.awt.geom.Rectangle2D; | |
| 63 | import java.io.IOException; | |
| 64 | import java.io.ObjectInputStream; | |
| 65 | import java.io.ObjectOutputStream; | |
| 66 | import java.io.Serializable; | |
| 67 | ||
| 68 | import org.jfree.chart.entity.EntityCollection; | |
| 69 | import org.jfree.chart.entity.PieSectionEntity; | |
| 70 | import org.jfree.chart.event.PlotChangeEvent; | |
| 71 | import org.jfree.chart.labels.PieToolTipGenerator; | |
| 72 | import org.jfree.chart.urls.PieURLGenerator; | |
| 73 | import org.jfree.chart.util.ObjectUtilities; | |
| 74 | import org.jfree.chart.util.PaintUtilities; | |
| 75 | import org.jfree.chart.util.RectangleInsets; | |
| 76 | import org.jfree.chart.util.Rotation; | |
| 77 | import org.jfree.chart.util.SerialUtilities; | |
| 78 | import org.jfree.chart.util.ShapeUtilities; | |
| 79 | import org.jfree.chart.util.UnitType; | |
| 80 | import org.jfree.data.pie.PieDataset; | |
| 81 | ||
| 82 | /** | |
| 83 | * A customised pie plot that leaves a hole in the middle. | |
| 84 | */ | |
| 85 | public class RingPlot extends PiePlot implements Cloneable, Serializable { | |
| 86 | ||
| 87 | /** For serialization. */ | |
| 88 | private static final long serialVersionUID =<PHONE_NUMBER>129676620L; | |
| 89 | ||
| 90 | /** | |
| 91 | * A flag that controls whether or not separators are drawn between the | |
| 92 | * sections of the chart. | |
| 93 | */ | |
| 94 | private boolean separatorsVisible; | |
| 95 | ||
| 96 | /** The stroke used to draw separators. */ | |
| 97 | private transient Stroke separatorStroke; | |
| 98 | ||
| 99 | /** The paint used to draw separators. */ | |
| 100 | private transient Paint separatorPaint; | |
| 101 | ||
| 102 | /** | |
| 103 | * The length of the inner separator extension (as a percentage of the | |
| 104 | * depth of the sections). | |
| 105 | */ | |
| 106 | private double innerSeparatorExtension; | |
| 107 | ||
| 108 | /** | |
| 109 | * The length of the outer separator extension (as a percentage of the | |
| 110 | * depth of the sections). | |
| 111 | */ | |
| 112 | private double outerSeparatorExtension; | |
| 113 | ||
| 114 | /** | |
| 115 | * The depth of the section as a percentage of the diameter. | |
| 116 | */ | |
| 117 | private double sectionDepth; | |
| 118 | ||
| 119 | /** | |
| 120 | * Creates a new plot with a null dataset. | |
| 121 | */ | |
| 122 | public RingPlot() { | |
| 123 | this(null); | |
| 124 | } | |
| 125 | ||
| 126 | /** | |
| 127 | * Creates a new plot for the specified dataset. | |
| 128 | * | |
| 129 | * @param dataset the dataset (null permitted). | |
| 130 | */ | |
| 131 | public RingPlot(PieDataset dataset) { | |
| 132 | super(dataset); | |
| 133 | this.separatorsVisible = true; | |
| 134 | this.separatorStroke = new BasicStroke(0.5f); | |
| 135 | this.separatorPaint = Color.gray; | |
| 136 | this.innerSeparatorExtension = 0.20; // twenty percent | |
| 137 | this.outerSeparatorExtension = 0.20; // twenty percent | |
| 138 | this.sectionDepth = 0.20; // 20% | |
| 139 | } | |
| 140 | ||
| 141 | /** | |
| 142 | * Returns a flag that indicates whether or not separators are drawn between | |
| 143 | * the sections in the chart. | |
| 144 | * | |
| 145 | * @return A boolean. | |
| 146 | * | |
| 147 | * @see #setSeparatorsVisible(boolean) | |
| 148 | */ | |
| 149 | public boolean getSeparatorsVisible() { | |
| 150 |
1
1. getSeparatorsVisible : replaced return of integer sized value with (x == 0 ? 1 : 0) → NO_COVERAGE |
return this.separatorsVisible; |
| 151 | } | |
| 152 | ||
| 153 | /** | |
| 154 | * Sets the flag that controls whether or not separators are drawn between | |
| 155 | * the sections in the chart, and sends a {@link PlotChangeEvent} to all | |
| 156 | * registered listeners. | |
| 157 | * | |
| 158 | * @param visible the flag. | |
| 159 | * | |
| 160 | * @see #getSeparatorsVisible() | |
| 161 | */ | |
| 162 | public void setSeparatorsVisible(boolean visible) { | |
| 163 | this.separatorsVisible = visible; | |
| 164 |
1
1. setSeparatorsVisible : removed call to org/jfree/chart/plot/RingPlot::fireChangeEvent → SURVIVED |
fireChangeEvent(); |
| 165 | } | |
| 166 | ||
| 167 | /** | |
| 168 | * Returns the separator stroke. | |
| 169 | * | |
| 170 | * @return The stroke (never null). | |
| 171 | * | |
| 172 | * @see #setSeparatorStroke(Stroke) | |
| 173 | */ | |
| 174 | public Stroke getSeparatorStroke() { | |
| 175 |
1
1. getSeparatorStroke : mutated return of Object value for org/jfree/chart/plot/RingPlot::getSeparatorStroke to ( if (x != null) null else throw new RuntimeException ) → NO_COVERAGE |
return this.separatorStroke; |
| 176 | } | |
| 177 | ||
| 178 | /** | |
| 179 | * Sets the stroke used to draw the separator between sections and sends | |
| 180 | * a {@link PlotChangeEvent} to all registered listeners. | |
| 181 | * | |
| 182 | * @param stroke the stroke (null not permitted). | |
| 183 | * | |
| 184 | * @see #getSeparatorStroke() | |
| 185 | */ | |
| 186 | public void setSeparatorStroke(Stroke stroke) { | |
| 187 |
1
1. setSeparatorStroke : negated conditional → KILLED |
if (stroke == null) { |
| 188 | throw new IllegalArgumentException("Null 'stroke' argument."); | |
| 189 | } | |
| 190 | this.separatorStroke = stroke; | |
| 191 |
1
1. setSeparatorStroke : removed call to org/jfree/chart/plot/RingPlot::fireChangeEvent → SURVIVED |
fireChangeEvent(); |
| 192 | } | |
| 193 | ||
| 194 | /** | |
| 195 | * Returns the separator paint. | |
| 196 | * | |
| 197 | * @return The paint (never null). | |
| 198 | * | |
| 199 | * @see #setSeparatorPaint(Paint) | |
| 200 | */ | |
| 201 | public Paint getSeparatorPaint() { | |
| 202 |
1
1. getSeparatorPaint : mutated return of Object value for org/jfree/chart/plot/RingPlot::getSeparatorPaint to ( if (x != null) null else throw new RuntimeException ) → NO_COVERAGE |
return this.separatorPaint; |
| 203 | } | |
| 204 | ||
| 205 | /** | |
| 206 | * Sets the paint used to draw the separator between sections and sends a | |
| 207 | * {@link PlotChangeEvent} to all registered listeners. | |
| 208 | * | |
| 209 | * @param paint the paint (null not permitted). | |
| 210 | * | |
| 211 | * @see #getSeparatorPaint() | |
| 212 | */ | |
| 213 | public void setSeparatorPaint(Paint paint) { | |
| 214 |
1
1. setSeparatorPaint : negated conditional → KILLED |
if (paint == null) { |
| 215 | throw new IllegalArgumentException("Null 'paint' argument."); | |
| 216 | } | |
| 217 | this.separatorPaint = paint; | |
| 218 |
1
1. setSeparatorPaint : removed call to org/jfree/chart/plot/RingPlot::fireChangeEvent → SURVIVED |
fireChangeEvent(); |
| 219 | } | |
| 220 | ||
| 221 | /** | |
| 222 | * Returns the length of the inner extension of the separator line that | |
| 223 | * is drawn between sections, expressed as a percentage of the depth of | |
| 224 | * the section. | |
| 225 | * | |
| 226 | * @return The inner separator extension (as a percentage). | |
| 227 | * | |
| 228 | * @see #setInnerSeparatorExtension(double) | |
| 229 | */ | |
| 230 | public double getInnerSeparatorExtension() { | |
| 231 |
1
1. getInnerSeparatorExtension : replaced return of double value with -(x + 1) for org/jfree/chart/plot/RingPlot::getInnerSeparatorExtension → NO_COVERAGE |
return this.innerSeparatorExtension; |
| 232 | } | |
| 233 | ||
| 234 | /** | |
| 235 | * Sets the length of the inner extension of the separator line that is | |
| 236 | * drawn between sections, as a percentage of the depth of the | |
| 237 | * sections, and sends a {@link PlotChangeEvent} to all registered | |
| 238 | * listeners. | |
| 239 | * | |
| 240 | * @param percent the percentage. | |
| 241 | * | |
| 242 | * @see #getInnerSeparatorExtension() | |
| 243 | * @see #setOuterSeparatorExtension(double) | |
| 244 | */ | |
| 245 | public void setInnerSeparatorExtension(double percent) { | |
| 246 | this.innerSeparatorExtension = percent; | |
| 247 |
1
1. setInnerSeparatorExtension : removed call to org/jfree/chart/plot/RingPlot::fireChangeEvent → SURVIVED |
fireChangeEvent(); |
| 248 | } | |
| 249 | ||
| 250 | /** | |
| 251 | * Returns the length of the outer extension of the separator line that | |
| 252 | * is drawn between sections, expressed as a percentage of the depth of | |
| 253 | * the section. | |
| 254 | * | |
| 255 | * @return The outer separator extension (as a percentage). | |
| 256 | * | |
| 257 | * @see #setOuterSeparatorExtension(double) | |
| 258 | */ | |
| 259 | public double getOuterSeparatorExtension() { | |
| 260 |
1
1. getOuterSeparatorExtension : replaced return of double value with -(x + 1) for org/jfree/chart/plot/RingPlot::getOuterSeparatorExtension → NO_COVERAGE |
return this.outerSeparatorExtension; |
| 261 | } | |
| 262 | ||
| 263 | /** | |
| 264 | * Sets the length of the outer extension of the separator line that is | |
| 265 | * drawn between sections, as a percentage of the depth of the | |
| 266 | * sections, and sends a {@link PlotChangeEvent} to all registered | |
| 267 | * listeners. | |
| 268 | * | |
| 269 | * @param percent the percentage. | |
| 270 | * | |
| 271 | * @see #getOuterSeparatorExtension() | |
| 272 | */ | |
| 273 | public void setOuterSeparatorExtension(double percent) { | |
| 274 | this.outerSeparatorExtension = percent; | |
| 275 |
1
1. setOuterSeparatorExtension : removed call to org/jfree/chart/plot/RingPlot::fireChangeEvent → SURVIVED |
fireChangeEvent(); |
| 276 | } | |
| 277 | ||
| 278 | /** | |
| 279 | * Returns the depth of each section, expressed as a percentage of the | |
| 280 | * plot radius. | |
| 281 | * | |
| 282 | * @return The depth of each section. | |
| 283 | * | |
| 284 | * @see #setSectionDepth(double) | |
| 285 | * @since 1.0.3 | |
| 286 | */ | |
| 287 | public double getSectionDepth() { | |
| 288 |
1
1. getSectionDepth : replaced return of double value with -(x + 1) for org/jfree/chart/plot/RingPlot::getSectionDepth → NO_COVERAGE |
return this.sectionDepth; |
| 289 | } | |
| 290 | ||
| 291 | /** | |
| 292 | * The section depth is given as percentage of the plot radius. | |
| 293 | * Specifying 1.0 results in a straightforward pie chart. | |
| 294 | * | |
| 295 | * @param sectionDepth the section depth. | |
| 296 | * | |
| 297 | * @see #getSectionDepth() | |
| 298 | * @since 1.0.3 | |
| 299 | */ | |
| 300 | public void setSectionDepth(double sectionDepth) { | |
| 301 | this.sectionDepth = sectionDepth; | |
| 302 |
1
1. setSectionDepth : removed call to org/jfree/chart/plot/RingPlot::fireChangeEvent → SURVIVED |
fireChangeEvent(); |
| 303 | } | |
| 304 | ||
| 305 | /** | |
| 306 | * Initialises the plot state (which will store the total of all dataset | |
| 307 | * values, among other things). This method is called once at the | |
| 308 | * beginning of each drawing. | |
| 309 | * | |
| 310 | * @param g2 the graphics device. | |
| 311 | * @param plotArea the plot area (null not permitted). | |
| 312 | * @param plot the plot. | |
| 313 | * @param index the secondary index (null for primary | |
| 314 | * renderer). | |
| 315 | * @param info collects chart rendering information for return to caller. | |
| 316 | * | |
| 317 | * @return A state object (maintains state information relevant to one | |
| 318 | * chart drawing). | |
| 319 | */ | |
| 320 | public PiePlotState initialise(Graphics2D g2, Rectangle2D plotArea, | |
| 321 | PiePlot plot, Integer index, PlotRenderingInfo info) { | |
| 322 | ||
| 323 | PiePlotState state = super.initialise(g2, plotArea, plot, index, info); | |
| 324 |
1
1. initialise : removed call to org/jfree/chart/plot/PiePlotState::setPassesRequired → NO_COVERAGE |
state.setPassesRequired(3); |
| 325 |
1
1. initialise : mutated return of Object value for org/jfree/chart/plot/RingPlot::initialise to ( if (x != null) null else throw new RuntimeException ) → NO_COVERAGE |
return state; |
| 326 | ||
| 327 | } | |
| 328 | ||
| 329 | /** | |
| 330 | * Draws a single data item. | |
| 331 | * | |
| 332 | * @param g2 the graphics device (null not permitted). | |
| 333 | * @param section the section index. | |
| 334 | * @param dataArea the data plot area. | |
| 335 | * @param state state information for one chart. | |
| 336 | * @param currentPass the current pass index. | |
| 337 | */ | |
| 338 | protected void drawItem(Graphics2D g2, | |
| 339 | int section, | |
| 340 | Rectangle2D dataArea, | |
| 341 | PiePlotState state, | |
| 342 | int currentPass) { | |
| 343 | ||
| 344 | PieDataset dataset = getDataset(); | |
| 345 | Number n = dataset.getValue(section); | |
| 346 |
1
1. drawItem : negated conditional → NO_COVERAGE |
if (n == null) { |
| 347 | return; | |
| 348 | } | |
| 349 | double value = n.doubleValue(); | |
| 350 | double angle1 = 0.0; | |
| 351 | double angle2 = 0.0; | |
| 352 | ||
| 353 | Rotation direction = getDirection(); | |
| 354 |
1
1. drawItem : negated conditional → NO_COVERAGE |
if (direction == Rotation.CLOCKWISE) { |
| 355 | angle1 = state.getLatestAngle(); | |
| 356 |
3
1. drawItem : Replaced double division with multiplication → NO_COVERAGE 2. drawItem : Replaced double multiplication with division → NO_COVERAGE 3. drawItem : Replaced double subtraction with addition → NO_COVERAGE |
angle2 = angle1 - value / state.getTotal() * 360.0; |
| 357 | } | |
| 358 |
1
1. drawItem : negated conditional → NO_COVERAGE |
else if (direction == Rotation.ANTICLOCKWISE) { |
| 359 | angle1 = state.getLatestAngle(); | |
| 360 |
3
1. drawItem : Replaced double division with multiplication → NO_COVERAGE 2. drawItem : Replaced double multiplication with division → NO_COVERAGE 3. drawItem : Replaced double addition with subtraction → NO_COVERAGE |
angle2 = angle1 + value / state.getTotal() * 360.0; |
| 361 | } | |
| 362 | else { | |
| 363 | throw new IllegalStateException("Rotation type not recognised."); | |
| 364 | } | |
| 365 | ||
| 366 |
1
1. drawItem : Replaced double subtraction with addition → NO_COVERAGE |
double angle = (angle2 - angle1); |
| 367 |
2
1. drawItem : changed conditional boundary → NO_COVERAGE 2. drawItem : negated conditional → NO_COVERAGE |
if (Math.abs(angle) > getMinimumArcAngleToDraw()) { |
| 368 | Comparable key = getSectionKey(section); | |
| 369 | double ep = 0.0; | |
| 370 | double mep = getMaximumExplodePercent(); | |
| 371 |
2
1. drawItem : changed conditional boundary → NO_COVERAGE 2. drawItem : negated conditional → NO_COVERAGE |
if (mep > 0.0) { |
| 372 |
1
1. drawItem : Replaced double division with multiplication → NO_COVERAGE |
ep = getExplodePercent(key) / mep; |
| 373 | } | |
| 374 | Rectangle2D arcBounds = getArcBounds(state.getPieArea(), | |
| 375 | state.getExplodedPieArea(), angle1, angle, ep); | |
| 376 | Arc2D.Double arc = new Arc2D.Double(arcBounds, angle1, angle, | |
| 377 | Arc2D.OPEN); | |
| 378 | ||
| 379 | // create the bounds for the inner arc | |
| 380 |
1
1. drawItem : Replaced double division with multiplication → NO_COVERAGE |
double depth = this.sectionDepth / 2.0; |
| 381 | RectangleInsets s = new RectangleInsets(UnitType.RELATIVE, | |
| 382 | depth, depth, depth, depth); | |
| 383 | Rectangle2D innerArcBounds = new Rectangle2D.Double(); | |
| 384 |
1
1. drawItem : removed call to java/awt/geom/Rectangle2D::setRect → NO_COVERAGE |
innerArcBounds.setRect(arcBounds); |
| 385 |
1
1. drawItem : removed call to org/jfree/chart/util/RectangleInsets::trim → NO_COVERAGE |
s.trim(innerArcBounds); |
| 386 | // calculate inner arc in reverse direction, for later | |
| 387 | // GeneralPath construction | |
| 388 |
2
1. drawItem : removed negation → NO_COVERAGE 2. drawItem : Replaced double addition with subtraction → NO_COVERAGE |
Arc2D.Double arc2 = new Arc2D.Double(innerArcBounds, angle1 |
| 389 | + angle, -angle, Arc2D.OPEN); | |
| 390 | GeneralPath path = new GeneralPath(); | |
| 391 |
1
1. drawItem : removed call to java/awt/geom/GeneralPath::moveTo → NO_COVERAGE |
path.moveTo((float) arc.getStartPoint().getX(), |
| 392 | (float) arc.getStartPoint().getY()); | |
| 393 |
1
1. drawItem : removed call to java/awt/geom/GeneralPath::append → NO_COVERAGE |
path.append(arc.getPathIterator(null), false); |
| 394 |
1
1. drawItem : removed call to java/awt/geom/GeneralPath::append → NO_COVERAGE |
path.append(arc2.getPathIterator(null), true); |
| 395 |
1
1. drawItem : removed call to java/awt/geom/GeneralPath::closePath → NO_COVERAGE |
path.closePath(); |
| 396 | ||
| 397 | Line2D separator = new Line2D.Double(arc2.getEndPoint(), | |
| 398 | arc.getStartPoint()); | |
| 399 | ||
| 400 |
1
1. drawItem : negated conditional → NO_COVERAGE |
if (currentPass == 0) { |
| 401 | Paint shadowPaint = getShadowPaint(); | |
| 402 | double shadowXOffset = getShadowXOffset(); | |
| 403 | double shadowYOffset = getShadowYOffset(); | |
| 404 |
1
1. drawItem : negated conditional → NO_COVERAGE |
if (shadowPaint != null) { |
| 405 | Shape shadowArc = ShapeUtilities.createTranslatedShape( | |
| 406 | path, (float) shadowXOffset, (float) shadowYOffset); | |
| 407 |
1
1. drawItem : removed call to java/awt/Graphics2D::setPaint → NO_COVERAGE |
g2.setPaint(shadowPaint); |
| 408 |
1
1. drawItem : removed call to java/awt/Graphics2D::fill → NO_COVERAGE |
g2.fill(shadowArc); |
| 409 | } | |
| 410 | } | |
| 411 |
1
1. drawItem : negated conditional → NO_COVERAGE |
else if (currentPass == 1) { |
| 412 | Paint paint = lookupSectionPaint(key, false); | |
| 413 |
1
1. drawItem : removed call to java/awt/Graphics2D::setPaint → NO_COVERAGE |
g2.setPaint(paint); |
| 414 |
1
1. drawItem : removed call to java/awt/Graphics2D::fill → NO_COVERAGE |
g2.fill(path); |
| 415 | Paint outlinePaint = lookupSectionOutlinePaint(key, false); | |
| 416 | Stroke outlineStroke = lookupSectionOutlineStroke(key, false); | |
| 417 |
2
1. drawItem : negated conditional → NO_COVERAGE 2. drawItem : negated conditional → NO_COVERAGE |
if (outlinePaint != null && outlineStroke != null) { |
| 418 |
1
1. drawItem : removed call to java/awt/Graphics2D::setPaint → NO_COVERAGE |
g2.setPaint(outlinePaint); |
| 419 |
1
1. drawItem : removed call to java/awt/Graphics2D::setStroke → NO_COVERAGE |
g2.setStroke(outlineStroke); |
| 420 |
1
1. drawItem : removed call to java/awt/Graphics2D::draw → NO_COVERAGE |
g2.draw(path); |
| 421 | } | |
| 422 | ||
| 423 | // add an entity for the pie section | |
| 424 |
1
1. drawItem : negated conditional → NO_COVERAGE |
if (state.getInfo() != null) { |
| 425 | EntityCollection entities = state.getEntityCollection(); | |
| 426 |
1
1. drawItem : negated conditional → NO_COVERAGE |
if (entities != null) { |
| 427 | String tip = null; | |
| 428 | PieToolTipGenerator toolTipGenerator | |
| 429 | = getToolTipGenerator(); | |
| 430 |
1
1. drawItem : negated conditional → NO_COVERAGE |
if (toolTipGenerator != null) { |
| 431 | tip = toolTipGenerator.generateToolTip(dataset, | |
| 432 | key); | |
| 433 | } | |
| 434 | String url = null; | |
| 435 | PieURLGenerator urlGenerator = getURLGenerator(); | |
| 436 |
1
1. drawItem : negated conditional → NO_COVERAGE |
if (urlGenerator != null) { |
| 437 | url = urlGenerator.generateURL(dataset, key, | |
| 438 | getPieIndex()); | |
| 439 | } | |
| 440 | PieSectionEntity entity = new PieSectionEntity(path, | |
| 441 | dataset, getPieIndex(), section, key, tip, | |
| 442 | url); | |
| 443 |
1
1. drawItem : removed call to org/jfree/chart/entity/EntityCollection::add → NO_COVERAGE |
entities.add(entity); |
| 444 | } | |
| 445 | } | |
| 446 | } | |
| 447 |
1
1. drawItem : negated conditional → NO_COVERAGE |
else if (currentPass == 2) { |
| 448 |
1
1. drawItem : negated conditional → NO_COVERAGE |
if (this.separatorsVisible) { |
| 449 | Line2D extendedSeparator = extendLine(separator, | |
| 450 | this.innerSeparatorExtension, | |
| 451 | this.outerSeparatorExtension); | |
| 452 |
1
1. drawItem : removed call to java/awt/Graphics2D::setStroke → NO_COVERAGE |
g2.setStroke(this.separatorStroke); |
| 453 |
1
1. drawItem : removed call to java/awt/Graphics2D::setPaint → NO_COVERAGE |
g2.setPaint(this.separatorPaint); |
| 454 |
1
1. drawItem : removed call to java/awt/Graphics2D::draw → NO_COVERAGE |
g2.draw(extendedSeparator); |
| 455 | } | |
| 456 | } | |
| 457 | } | |
| 458 |
1
1. drawItem : removed call to org/jfree/chart/plot/PiePlotState::setLatestAngle → NO_COVERAGE |
state.setLatestAngle(angle2); |
| 459 | } | |
| 460 | ||
| 461 | /** | |
| 462 | * This method overrides the default value for cases where the ring plot | |
| 463 | * is very thin. This fixes bug 2121818. | |
| 464 | * | |
| 465 | * @return The label link depth, as a percentage of the plot's radius. | |
| 466 | */ | |
| 467 | protected double getLabelLinkDepth() { | |
| 468 |
2
1. getLabelLinkDepth : Replaced double division with multiplication → NO_COVERAGE 2. getLabelLinkDepth : replaced return of double value with -(x + 1) for org/jfree/chart/plot/RingPlot::getLabelLinkDepth → NO_COVERAGE |
return Math.min(super.getLabelLinkDepth(), getSectionDepth() / 2); |
| 469 | } | |
| 470 | ||
| 471 | /** | |
| 472 | * Tests this plot for equality with an arbitrary object. | |
| 473 | * | |
| 474 | * @param obj the object to test against (null permitted). | |
| 475 | * | |
| 476 | * @return A boolean. | |
| 477 | */ | |
| 478 | public boolean equals(Object obj) { | |
| 479 |
1
1. equals : negated conditional → KILLED |
if (this == obj) { |
| 480 |
1
1. equals : replaced return of integer sized value with (x == 0 ? 1 : 0) → NO_COVERAGE |
return true; |
| 481 | } | |
| 482 |
1
1. equals : negated conditional → KILLED |
if (!(obj instanceof RingPlot)) { |
| 483 |
1
1. equals : replaced return of integer sized value with (x == 0 ? 1 : 0) → KILLED |
return false; |
| 484 | } | |
| 485 | RingPlot that = (RingPlot) obj; | |
| 486 |
1
1. equals : negated conditional → KILLED |
if (this.separatorsVisible != that.separatorsVisible) { |
| 487 |
1
1. equals : replaced return of integer sized value with (x == 0 ? 1 : 0) → KILLED |
return false; |
| 488 | } | |
| 489 |
1
1. equals : negated conditional → KILLED |
if (!ObjectUtilities.equal(this.separatorStroke, |
| 490 | that.separatorStroke)) { | |
| 491 |
1
1. equals : replaced return of integer sized value with (x == 0 ? 1 : 0) → KILLED |
return false; |
| 492 | } | |
| 493 |
1
1. equals : negated conditional → KILLED |
if (!PaintUtilities.equal(this.separatorPaint, that.separatorPaint)) { |
| 494 |
1
1. equals : replaced return of integer sized value with (x == 0 ? 1 : 0) → KILLED |
return false; |
| 495 | } | |
| 496 |
1
1. equals : negated conditional → KILLED |
if (this.innerSeparatorExtension != that.innerSeparatorExtension) { |
| 497 |
1
1. equals : replaced return of integer sized value with (x == 0 ? 1 : 0) → KILLED |
return false; |
| 498 | } | |
| 499 |
1
1. equals : negated conditional → KILLED |
if (this.outerSeparatorExtension != that.outerSeparatorExtension) { |
| 500 |
1
1. equals : replaced return of integer sized value with (x == 0 ? 1 : 0) → KILLED |
return false; |
| 501 | } | |
| 502 |
1
1. equals : negated conditional → KILLED |
if (this.sectionDepth != that.sectionDepth) { |
| 503 |
1
1. equals : replaced return of integer sized value with (x == 0 ? 1 : 0) → KILLED |
return false; |
| 504 | } | |
| 505 |
1
1. equals : replaced return of integer sized value with (x == 0 ? 1 : 0) → KILLED |
return super.equals(obj); |
| 506 | } | |
| 507 | ||
| 508 | /** | |
| 509 | * Creates a new line by extending an existing line. | |
| 510 | * | |
| 511 | * @param line the line (null not permitted). | |
| 512 | * @param startPercent the amount to extend the line at the start point | |
| 513 | * end. | |
| 514 | * @param endPercent the amount to extend the line at the end point end. | |
| 515 | * | |
| 516 | * @return A new line. | |
| 517 | */ | |
| 518 | private Line2D extendLine(Line2D line, double startPercent, | |
| 519 | double endPercent) { | |
| 520 |
1
1. extendLine : negated conditional → NO_COVERAGE |
if (line == null) { |
| 521 | throw new IllegalArgumentException("Null 'line' argument."); | |
| 522 | } | |
| 523 | double x1 = line.getX1(); | |
| 524 | double x2 = line.getX2(); | |
| 525 |
1
1. extendLine : Replaced double subtraction with addition → NO_COVERAGE |
double deltaX = x2 - x1; |
| 526 | double y1 = line.getY1(); | |
| 527 | double y2 = line.getY2(); | |
| 528 |
1
1. extendLine : Replaced double subtraction with addition → NO_COVERAGE |
double deltaY = y2 - y1; |
| 529 |
2
1. extendLine : Replaced double multiplication with division → NO_COVERAGE 2. extendLine : Replaced double subtraction with addition → NO_COVERAGE |
x1 = x1 - (startPercent * deltaX); |
| 530 |
2
1. extendLine : Replaced double multiplication with division → NO_COVERAGE 2. extendLine : Replaced double subtraction with addition → NO_COVERAGE |
y1 = y1 - (startPercent * deltaY); |
| 531 |
2
1. extendLine : Replaced double multiplication with division → NO_COVERAGE 2. extendLine : Replaced double addition with subtraction → NO_COVERAGE |
x2 = x2 + (endPercent * deltaX); |
| 532 |
2
1. extendLine : Replaced double multiplication with division → NO_COVERAGE 2. extendLine : Replaced double addition with subtraction → NO_COVERAGE |
y2 = y2 + (endPercent * deltaY); |
| 533 |
1
1. extendLine : mutated return of Object value for org/jfree/chart/plot/RingPlot::extendLine to ( if (x != null) null else throw new RuntimeException ) → NO_COVERAGE |
return new Line2D.Double(x1, y1, x2, y2); |
| 534 | } | |
| 535 | ||
| 536 | /** | |
| 537 | * Provides serialization support. | |
| 538 | * | |
| 539 | * @param stream the output stream. | |
| 540 | * | |
| 541 | * @throws IOException if there is an I/O error. | |
| 542 | */ | |
| 543 | private void writeObject(ObjectOutputStream stream) throws IOException { | |
| 544 |
1
1. writeObject : removed call to java/io/ObjectOutputStream::defaultWriteObject → KILLED |
stream.defaultWriteObject(); |
| 545 |
1
1. writeObject : removed call to org/jfree/chart/util/SerialUtilities::writeStroke → KILLED |
SerialUtilities.writeStroke(this.separatorStroke, stream); |
| 546 |
1
1. writeObject : removed call to org/jfree/chart/util/SerialUtilities::writePaint → KILLED |
SerialUtilities.writePaint(this.separatorPaint, stream); |
| 547 | } | |
| 548 | ||
| 549 | /** | |
| 550 | * Provides serialization support. | |
| 551 | * | |
| 552 | * @param stream the input stream. | |
| 553 | * | |
| 554 | * @throws IOException if there is an I/O error. | |
| 555 | * @throws ClassNotFoundException if there is a classpath problem. | |
| 556 | */ | |
| 557 | private void readObject(ObjectInputStream stream) | |
| 558 | throws IOException, ClassNotFoundException { | |
| 559 |
1
1. readObject : removed call to java/io/ObjectInputStream::defaultReadObject → KILLED |
stream.defaultReadObject(); |
| 560 | this.separatorStroke = SerialUtilities.readStroke(stream); | |
| 561 | this.separatorPaint = SerialUtilities.readPaint(stream); | |
| 562 | } | |
| 563 | ||
| 564 | } | |
Mutations | ||
| 150 |
1.1 |
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| 164 |
1.1 |
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| 175 |
1.1 |
|
| 187 |
1.1 |
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| 191 |
1.1 |
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| 202 |
1.1 |
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| 214 |
1.1 |
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| 218 |
1.1 |
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| 231 |
1.1 |
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| 247 |
1.1 |
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| 260 |
1.1 |
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| 275 |
1.1 |
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| 288 |
1.1 |
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| 302 |
1.1 |
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| 324 |
1.1 |
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| 325 |
1.1 |
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| 346 |
1.1 |
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| 354 |
1.1 |
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| 356 |
1.1 2.2 3.3 |
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| 358 |
1.1 |
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| 360 |
1.1 2.2 3.3 |
|
| 366 |
1.1 |
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| 367 |
1.1 2.2 |
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| 371 |
1.1 2.2 |
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| 372 |
1.1 |
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| 380 |
1.1 |
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| 384 |
1.1 |
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| 385 |
1.1 |
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| 388 |
1.1 2.2 |
|
| 391 |
1.1 |
|
| 393 |
1.1 |
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| 394 |
1.1 |
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| 395 |
1.1 |
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| 400 |
1.1 |
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| 404 |
1.1 |
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| 407 |
1.1 |
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| 408 |
1.1 |
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| 411 |
1.1 |
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| 413 |
1.1 |
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| 414 |
1.1 |
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| 417 |
1.1 2.2 |
|
| 418 |
1.1 |
|
| 419 |
1.1 |
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| 420 |
1.1 |
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| 424 |
1.1 |
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| 426 |
1.1 |
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| 430 |
1.1 |
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| 436 |
1.1 |
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| 443 |
1.1 |
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| 447 |
1.1 |
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| 448 |
1.1 |
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| 452 |
1.1 |
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| 453 |
1.1 |
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| 454 |
1.1 |
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| 458 |
1.1 |
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| 468 |
1.1 2.2 |
|
| 479 |
1.1 |
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| 480 |
1.1 |
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| 482 |
1.1 |
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| 483 |
1.1 |
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| 486 |
1.1 |
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| 487 |
1.1 |
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| 489 |
1.1 |
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| 491 |
1.1 |
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| 493 |
1.1 |
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| 494 |
1.1 |
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| 496 |
1.1 |
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| 497 |
1.1 |
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| 499 |
1.1 |
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| 500 |
1.1 |
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| 502 |
1.1 |
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| 503 |
1.1 |
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| 505 |
1.1 |
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| 520 |
1.1 |
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| 525 |
1.1 |
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| 528 |
1.1 |
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| 529 |
1.1 2.2 |
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| 530 |
1.1 2.2 |
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| 531 |
1.1 2.2 |
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| 532 |
1.1 2.2 |
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| 533 |
1.1 |
|
| 544 |
1.1 |
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| 545 |
1.1 |
|
| 546 |
1.1 |
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| 559 |
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We'll be posting all announcements and important information in our Discord Server. Be sure to join it.
Be sure to check out our schedule! We are hosting various talks, workshops, and games during the event! Some of the workshops might help you develop your project!
Make sure to join our Devpost! Devpost is where submissions will be sent at the end of the event!
Make sure your resume is attached to your application if you are looking for full time jobs or internships! You can edit your application by going to khe.io/apply when logged in.
We'll be posting announcements in discord!
Feel free to
contact us
if you have any questions!
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There are over 250,000 acres of agricultural residue burned every year in Washington State.
In 2012, farmers in Eastern Washington purchased and obtained permits to burn over 1 million tons of residual straw.
This puts approximately 45,000 tons of ash, particulates, carbon monoxide and carbon dioxide into the atmosphere every year.
250,000 tons of straw to produce pulp will save approx. 18,000 tons of these compounds from entering the atmosphere annually.
The products produced by Columbia Pulp will be fully recyclable and compostable and FSC Certified. The Forest Stewardship Council manages a rigorous, credible, third party certification system that sets standards for responsible forest management. A voluntary program, FSC uses the power of the marketplace to protect forests for future generations. Due to the FSC Certification, Columbia Pulp products will be uniquely positioned to appeal to large consumer packaging companies, who seek ways to reduce the environmental impact of their supply chains.
The amount of straw used by Columbia Pulp for manufacturing market pulp and then paper products will replace approximately 280,000 tons of wood chips, positively impacting forest sustainability. Columbia facility is a state-of-the-art facility using self-contained, energy efficient processes. The facility will use less energy and require less chemical than a traditional pulp mill. The energy consumption come from electricity and natural gas. Much of the residual product from pulping will be sold or utilized. Columbia will have the ability to sell the carbohydrate and lignin residue from the manufacturing process. Traditional pulp mills can produce an unseemly odor in the surrounding area. Columbia’s proprietary technology does not produce these odors as a result of the pulping as this is not a sulfur-based manufacturing process.


Hello, I'm Allison! I am an alum and was a captain of the Temple City HS Science Olympiad team from Southern California. Science Olympiad was undoubtedly the defining experience of my high school career, and I am beyond excited to take it to the next step in college with this incredible opportunity to make this tournament a reality. Science on!
Favorite events: Forensics, Invasive Species, anything ID really :)
Fun fact: I guess I'm a meme? ¯\_(ツ)_/¯
Hi, my name is Abraham Chen, I’m a Mounds View Scioly Alumni, and I’ve been involved in Scioly all through high school. One time my team’s Scioly bus got stranded on a highway for 7 hours, making our trip to a tournament 21+ hours. That’s dedication.
Favorite events: Forensics, Fossils and Experimental Design
Fun fact: I like heights because it scares me.
Hi I’m Colin and I love microbes. I competed for Wellwood Middle School and Fayetteville-Manlius High School and I graduated with a BS in microbiology from Cornell University last May. For three years, I also helped lead Science Olympiad at Cornell where I served as co-president 2015-16. Did I mention I love microbes?
Favorite events: Microbe Mission, Fermi Questions, Thermodynamics
Fun fact: I have a buzzcut in my picture because my Fermi partner and I agreed to shave each other’s heads if we got 1st in Fermi at States 2013. The rest is history.
Hi, my name is Dhruv, I’m a LASA Scioly Alumni, and I’ve been involved in Scioly all through high school. Science Olympiad helped me find my passion for biology and had led me to a career in medicine.
Favorite events: Anatomy and Physiology, Protein Modeling, Material Science
Fun fact: Most nights you can find me DJing in SF or LA at salsa and bachata clubs.
Hi, my name is Kathryn and I’m a Palo Alto Science Olympiad alum. In my three years on the team, I’ve competed in a variety of study, lab, and build events, from Chem Lab to Bridge Building. Science Olympiad has been the most important influencer of my college career, so I am incredibly excited to give back to the community through this tournament!
Favorite events: Forensics, Chem Lab, Protein Modeling, Bridge Building
Fun fact: I like to figure skate in my free time!
Hey, I’m Kevin (“KTien” since there are two Kevins on our executive board). I’m originally from Louisville, Kentucky, where I helped captain DuPont Manual High School’s Science Olympiad team. Outside of academics, I enjoy volunteering for Cardinal Free Clinics, skying fools for Stanford’s Ultimate Frisbee team, and searching for nearby boba tea cafés.
Favorite events: MagLev, It’s About Time, Chemistry Lab, Pickup Frisbee Before Awards
Fun fact: I do research on the African Cichlid fish, though I’m deathly icthyophobic
Hello! I'm a LASA alum and was captain of LASA's Science Olympiad team during 2015 and 2016. I did build events, which means that most of my SciO memories involve soldering in a hotel room bathtub, getting gliders stuck in chandeliers, or getting kicked out of Wal-Mart at 1 AM (sometimes you gotta test Scrambler the night before...) Science Olympiad has been a pretty big part of my life (in both the good and bad ways), so I'm glad to be helping run this tournament.
Favorite events: Scrambler, Mission Possible
Hi! I'm Rohan, and I’m an alum of Seven Lakes High School and Beckendorff Junior High in Katy, Texas. Throughout my Science Olympiad experience, I’ve competed extensively in building events such as Gravity Vehicle and Scrambler as well in as lab events like Chemistry Lab and Forensics. I had a blast organizing our inaugural tournament for you all, and I'm looking forward to making our next tournament even greater!
Favorite events: Forensics and Chem Lab
Fun fact: I love anything and everything Drake.
Hello! Call me Kevin or kmeng! I’m an Ed W. Clark HS Scioly alumni and captain. In preparation for state and nationals, I’ve stayed at school so late that midnight snacks and basketball with the cleaning staff is a norm. I’ve been told before that my love for Science Olympiad is “infectious”. Spread the love and stay committed!
Favorite events: Gravity Vehicle, Elastic Launched Glider, Wright Stuff, WIDI
Fun fact: My team and I won a state championship in soccer! I have a fierce passion for music and sports. Play me.
Hi, I’m Sarah Gurev, and I'm a Mira Loma High School and Winston Churchill Middle School Scioly alum. I've had the opportunity to attend Nationals five times, and the distinct pleasure of competing in every single water event each time. Science Olympiad was such a large part of my life, so I'm grateful it's not quite over yet. I'm really excited to help bring this tournament to life!
Favorite events: Protein Modeling, Awesome Aquifers, Dynamic Planet
Fun fact: I was very overprotective of my protein model, possibly because I was constantly worrying that my cat would eat it.
Hello! I'm a LASA alum and was captain of LASA's Science Olympiad team during 2015 and 2016. I did build events, which means that most of my SciO memories involve soldering in a hotel room bathtub, getting gliders stuck in chandeliers, or getting kicked out of Wal-Mart at 1 AM (sometimes you gotta test Scrambler the night before...) Science Olympiad has been a pretty big part of my life (in both the good and bad ways), so I'm glad to be helping run this tournament.
Favorite events: Scrambler, Mission Possible
Hey, I'm Ken! I'm a Palo Alto HS Scioly alum. Currently a junior at UC Berkeley studying Computer Science. Also like to study food, traveling, books, and anything else to keep me from being bored.
Hi! I'm David, and I'm an alum from Mira Loma High school. Science Olympiad was a big part of my life, and I spent countless hours doing almost every build event, so I've had my fair share of all nighters and last minute runs to Home Depot. Even though Science Olympiad caused me sleep deprivation and a whole lotta stress, I loved every second of it, and I look forward to continuing my Sci Oly career as an event coordinator for Helicopters!
Favorite events: Robot Arm, Wright Stuff, Electric Vehicle
Hi, I'm Shaina, a graduate of Mira Loma High school. After being asked to fill in for my school's SciOly B team at Regionals my junior year, I joined the the team as a senior and had the distinct pleasure of getting to compete at nationals in Cell biology and Protein Modeling. After this great experience, I was thrilled to write the Microbe Mission test at GGSO last year, and am really excited to be participating again this year.
Fun fact: My second memory is of the SF Giants losing the World Series in 2002, so I guess you could say I'm passionate about baseball.
A UC Berkeley alumnus, David Mrdjenovich is excited to continue his education as a first year PhD student in Materials Science at Cal, working towards a career in academia. Engaging in computational materials science research, David is an active member of the Persson Group at Lawrence Berkeley National Labs, investigating the properties of high-symmetry deformational pathways in crystals. As part of the Persson Group, David also contributes to the development of the Materials Project, an infrastructure for materials computation and a public repository of materials properties. Apart from research, David is passionate about sharing science with the larger community and is happy to assist in the coordination of the 2017 Golden Gate Science Olympiad competition.
Hey, I'm Nick! I've had the joy of competing in Science Olympiad for 3 years at Troy High school in Fullerton, CA, during which I got to be a part of two Nationals-winning teams. I captained the team in my senior year and enjoyed competing in my favorite events It's About Time and Compound Machines, along with some more frustrating/hellish events like Anatomy, MagLev (1st place at Nats, yay!), Experimental Design, and Bridges.
Fun fact: My MagLev car for Nationals 201 was made to work (at 2 am on the day of Nationals) with the help of a component salvaged from a Happy Meal toy.
Hi! Over the course of 6 years, I competed for Winston Churchill Middle school and Mira Loma High school in Sacramento, CA. In those years, Science Olympiad became the most important influence on my collegiate and career choices leading me to where I am today. I hope to contribute to Science Olympiad to further impose the competition's positive influence on other students to help them discover their passions.
Favorite events: Fossils, Technical Problem Solving, Remote Sensing
Hi, my name is Alvin Hsu, and I'm a Mission San Jose Scioly alum. I had the pleasure of competing in lab events, such as Chemistry Lab, Experimental Design, and Technical Problem Solving. I also got to try my hand at a couple build events, such as Mission Possible, but we don't like to talk about how those went.
Favorite events: Chemistry Lab, Experimental Design
Fun fact: My research involves many compounds that are pyrophoric, which means they catch on fire when they touch air!
I’m an alum from the North Hollywood HGM and the former team captain of their Science Olympiad team. I was on Oly for three years and it was the best thing I have ever devoted myself to being a part of. It made me much more knowledgeable in the sciences that I love and helped me meet amazing people. Now I hope to use my skills to give you all the best experience with Rocks and Minerals at GGSO.
Favorite events: Fossils, Rocks and Minerals, Invasive Species (and all other ID and Geo events :D)
Fun fact: My spirit animal is a taco.
Hi, I’m Tad Komacek! I’m a graduate student at the University of Arizona and a graduate of Harriton High school. I’ve been co-supervising the Astronomy C event at the National Science Olympiad since 2010, and am excited to come back to Golden Gate.
Favorite events: Astronomy, Dynamic Planet, and Remote Sensing
Hi! I’m a heart surgeon-in- training at Stanford University, and my interests include both adult and pediatric heart surgery, and developing new surgical technologies for use in the operating room. I previously competed in Scioly for Naperville Central High school (IL), leading the team to its first ever Nationals appearance in 2007. I’m still involved with Scioly as a coach for Palo Alto High school, and I hope to continue contributing at Scioly tournaments of all levels in the future.
Favorite events: Astronomy, Forensics, Fermi Questions, Storm the Castle, Sounds of Music
Fun fact: I’m also an orchestral conductor.
What's up! I'm from Orange, California where I was a co-captain of Troy Scioly. It was definitely a big part of my high school years, and now I'm really excited to come back to help.
Hi, my name is Shaina, and I am an alumna of Pembroke Hill, located in Kansas City, Missouri. Science Olympiad was a defining extracurricular for me throughout middle and high school, as I was able to travel with my best friends to several cities for the nationals tournament each year. I look forward to helping with Science Olympiad and giving back to one of my favorite parts of high school! :)
Favorite events: Experimental Design, Optics, Write It Do It, Protein Modeling
Fun fact: In my free time, I sing and participate in Bollywood dancing. Yes, I am from the Midwest, and no, I don’t live on a farm.
Hey I'm Sarina! I am an alum and former captain of the Columbia team in NY. I've been involved in Scioly since middle school. The highlight of my Scioly career was when I got to frolic with baby cows with my friends in Wisconsin.
Favorite events: Protein Modeling, Anatomy and Physiology, Game On
Fun fact: I have a profound appreciation for arugula
Hi, my name is Mindy and I graduated high school in 2011 from Peak to Peak Charter school in Lafayette, Colorado. My crowning achievements with Science Olympiad include forgetting Godzilla's hometown, mistaking a protein for the USS Enterprise, and baking a complete periodic table of cupcakes.
Favorite events: Herpetology, Ornithology
Fun fact: I'm still waiting for SciOly to introduce Chiropterology…
In high school, Science Olympiad played an important role in my decision to pursue a career in science. I'm currently a graduate student in the physics department at UC Berkeley. I was lucky enough to compete at 2010 and 2011 nationals with my team from Acton-Boxborough Regional High school in Massachusetts. I have many fond memories with my team, both in competition and just hanging out. I'm glad to be able to stay involved with SciOly through GGSO!
Favorite events: wind power, chem lab
Hi, I'm Alex Settle. I was the co-captain of the Harriton High school Science Olympiad Team in 2012 and graduated with a B.S. in Chemical Engineering from Cornell in 2016. Science Olympiad was a huge part of my life and I would not be the person I am today without it.
Favorite events: Sounds of Music, Fermi Questions, Ornithology
Fun fact: There's stock audio of a Whip-poor-will in Zelda":" Breath of the Wild and I only know that because of Science Olympiad
Hi! I'm Saarang, and I’m an alum of Clements High school in Sugar Land, TX. Science Olympiad was one of my most defining experiences in High school, and I'm super excited to continue in college! I'm looking forward to helping others get the same, awesome experience in Science Olympiad as I did.
Fun fact: I have blue hair in this picture because our team agreed to dye it if we made nats :)
Hello, I’m Varun. I’m an alum and former captain of LASA. I’ve been involved with Science Olympiad for 7 years and the three years I spent in high school making enormous binders and staying up until 2 AM the night before a competition cramming, finishing binders, and taking tests were some of the most rewarding and defining moments of my high school experience. I’ve had the great honor of competing at Nationals three times and I'm excited to be able to write exams and improve the experience of current competitors.
Favorite events: Fossils, Rocks and Minerals, Invasive Species, Anatomy and Physiology
Fun fact: The majority of my Fossils binder was handwritten (I know it sounds odd, but it worked), so whenever I took it to competition it was always carried inside a small duffel bag to protect it from the elements.
Hello, I'm Faith, a former six-year member of Science Olympiad and a captain of the Century High school team from Bismarck, North Dakota. What I learned in SciOly's never really left my life--love for science, skills and random facts alike. Sometimes knowing the Mohs scale of mineral hardness or the redshift effect of universe expansion proves useful!
Favorite events: Anatomy and Physiology, Astronomy, Rocks and Minerals, Dynamic Planet
Fun fact: I write sci-fi and fantasy books in my free time!
Hi!! I'm Evelyn Zhang, a Sci Oly alum from Mira Loma High school. It's an understatement to say that Science Olympiad changed my life, and I can't wait to keep doing what I love the most :)
Favorite events: Anatomy and Physiology, Disease Detectives, Experimental Design
Hello world, I’m Grant, former SciOly captain at Valencia High! My SciOly adventure began at Kraemer Middle, and I gradually found a passion for build events at Valencia High (competed 6 years). I still remember the long hours I spent soldering wires, gluing bridge joints with impossible precision, and getting wright stuff planes stuck in the ceiling. I have also helped coach the Kraemer team for 4 years and helped coordinate the CA Inland Empire Regionals and invitationals in the OC region. Overall, my SciOly experience has been a blast and I look forward to continuing to work with students in the community!
Favorite events: Robot Arm, Wright Stuff, and Bridge Building
Fun fact: I love solving Rubik’s Cubes and writing/blogging!
Hi! I’m Alexis and I was captain of my SAS High school team from Florida. I competed in Science Olympiad all throughout high school and loved every moment of it! I can’t wait to help out again this year with GGSO.
Favorite events: Forensics, Chemistry Lab, and Helicopters
Hi, I’m Jared. I am a Troy High school alumni and captain, and I competed in all things physics and computer science. Science Olympiad has been one of my most cherished high school experiences, and I’m excited to continue to contribute!
Favorite events: Optics, Robot Arm, Hovercraft
Fun fact: I’m a published game developer!
Hi, I'm Chris, and I'm a three year veteran of Science Olympiad at North Hollywood High school in Southern California. I was captain of my team for two years and worked on build events for the majority of my Science Olympiad career. I look forward to fostering the inquisitive atmosphere that is inherent in the Science Olympiad culture. Science Olympiad made my high school experience incredibly enjoyable and I will do my best to enhance the overall experience at our competition.
Favorite events: Wright Stuff, Robot Arm, Dynamic Planet, Hydrogeology, Elastic Launch Glider
Fun fact: I play club frisbee for UC Berkeley, and I'm always willing to throw!
I am an undergraduate at UC Berkeley and intends to double major in Legal Studies and Business. I have participated in Science Olympiad for 8 years and is excited to continue being involved with the competition. I hopes to eventually work in women's sports.
Hey, I'm Tyler and I'm a Mounds View Science Olympiad alum. SciOly was probably the biggest part of my high school experience, so I'm looking forward to helping with GGSO!
Favorite events: Wind Power, It's About Time, Green Generation
Golden Gate Science Olympiad is run by Golden Gate Science League, a California 501(c)(3) nonprofit.
Questions? Interested in sponsoring? Joining us as a student at Stanford or UC Berkeley next year? Shoot us an email to get involved with our team!


Lacu is a project by MinnPost for Northern Spark 2014 which visualizes 13,817 lakes in Minnesota, two at a time. Lacu ran from sun up to sun down, the span of the event. The plan was to project it on the front of the Northrup Auditorium, but due to weather, we projected it inside the building. It starts at the smallest lakes and ends with Lake Superior. For details on what data is being represented, see this image.
This project was not browser tested and will only run on modern web browsers and newer computers. It is meant for a fixed width of 1440px. It requires loading of over 30MB of data and will take some time to load; please be patient. For demo purposes, it will start at a random point in the presentation. Source code on Github.


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