He told me to inhale as much as I could, breathe it in, breathe it in deeply. I held it. Something happened, and I remember saying 'Woah', and hearing people around me say something like 'Haha, she said woah!' - except I couldn't hold on to the voices, they swam around my head and echoed through every part of myself and the universe.
Then, blackness. Later I was told it was blackness with a smile.
When I finally regained control of my head, I was floating, I was one with the universe, and my vision returned, giving me images of what I was floating within, blue sky and thick cotton ball clouds. They were almost digital, warping in to each other and myself. I had no awareness of my physical body. Things were good, briefly. Not that I knew this at the time. I recall no thought process, I wasn't thinking at all, just simply experiencing.
Then something snapped, perhaps the return to the physical realm. I became aware of my breathing, it was heavy and uneven. I looked down at my hands and somewhere along the way I had dropped what I was holding and my fists were clenched. I tried to lift my head but it felt like a weight, it felt un-naturally heavy. I lay back and told myself to enjoy the ride, I knew what I was doing, and I should appreciate the experience while it lasted. It didn't work. Panic and terror set in. I knew that I was messing with a chemical released in the brain when you die, so I somehow convinced myself that I wasn't going to survive.
I was so hot, so very hot. It took me a long time to figure out that that was what was wrong. I looked around and apparently had held a captive audience, including my best friend who had not been there when I first inhaled. She looked worried. I thought about speaking, but it was like my mouth wasn't connected to my brain. I knew what I wanted to say, and it was simple enough - water, water - but nothing came out, until I said 'I don't like this' without realising. I said it a few more times, and then asked for water.
It was then I started to feel the worst nausea I have ever felt. I ran to the toilet and vomited everywhere, and felt immediately better. Emotionally though, I was broken. After a good cry I looked up, and those cotton ball clouds I had appreciated earlier had formed into looming dark storm clouds.
I felt the storm coming. And I was reborn. What a perfect beginning.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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{% extends "info_base.html" %} {% block title %}Puzzlehunt CMU{% endblock title %} {% block includes %} {% endblock includes %} {% block content %}
Our next hunt:
{{curr_hunt.hunt_name}}
{{ curr_hunt.display_start_date|date:" m/d/y " }} {{ curr_hunt.display_start_date|time:"h:iA" }} - {{ curr_hunt.display_end_date|time:"h:iA" }}
Starts in {{ curr_hunt.location }}
We are PuzzlehuntCMU, a group of puzzle enthusiasts from Carnegie Mellon University. We work together to solve Puzzle Hunts such as the MIT mystery hunt, or any of the many tech company puzzlehunts (examples of such companies include Microsoft, APT and Palantir). We also write and host CMU's very own Puzzlehunt once a semester, with the goal of providing a fun-filled experience to as many members of the community as possible.
Very simply put, it's a full-day event where people get together to solve carefully designed puzzles which somehow link together on a larger scale.
A puzzlehunt is typically characterized by its structure. In addition to several normal puzzles, each round has a meta-puzzle, which requires teams to have solved most of the normal puzzles to even unlock, and often requires solvers to incorporate answers from that round's normal puzzles to make sense of.
Our puzzlehunt's structure changes from semester to semester so as to provide new challenges for solvers.
Yeah, check out our current hunt page!
Email the HALP?! LINE<EMAIL_ADDRESS>with "Puzzle Hunt" somewhere in the subject line.


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class Money {
int euro;
Money(int euro) { this.euro = euro; }
}
public static Money sum(List<Money> xs) { ... }
Money total = sum(Arrays.asList(new Money(1), new Money(2)));
public static <T> T sum(List<T> ts) { ... }
T + T ???
implement interface
interface Addable<T> {
T add(T t);
}
public static <T extends Addable<T>> T sum(List<T> ts) {
T ret = ts.get(0);
for (int i = 1; i < ts.size(); ++i) { ret = ret.add(ts.get(i)); }
return ret;
}
small print: don't call with empty list
unless...
The most disastrous thing that you can ever learn is your first programming language.
Alan Kay
Always consider what you would do in Haskell
http://akisaarinen.fi/blog/2012/09/10/haskellify-your-scala/
REPL! (or look down...)
Prelude> :t sum
sum :: Num a => [a] -> a
Prelude> :info Num
class Num a where
(+) :: a -> a -> a
...
ah, Num knows how to add two as...
...but is this just like Addable in Java?
...not quite...
interface Addable<T> {
T add(T t);
}
public static <T extends Addable<T>> T sum(List<T> ts) {
Haskell doesn't have classes
as we know them from other languages.
And no inheritance.
In Haskell, a
separate concept (Num)
defines how to add things.
One or more definitions of adding can be
separately
defined for a custom type Money.
Function arguments can be restricted with these
type classes.
interface Adder<T> {
T add(T t1, T t2); // Look, mom, now with two arguments!
}
public static <T> T sum(List<T> ts, Adder<T> adder) {
T ret = ts.get(0);
for (int i = 1; i < ts.size(); ++i) { ret = adder.add(ret, ts.get(i)); }
return ret;
}
now adding is separated from the class definition. Ha!
...but the trade-off is an additional function parameter...
in Java, these two strategies are often combined,
e.g. Collections.sort
public static <T extends Comparable<? super T>> void sort(List<T> list) { ... }
public static <T> void sort(List<T> list, Comparator<? super T> c) { ... }
REPL! (or look down...)
scala> def sum[T](ts: List[T]): T = ???
same problem as in Java, forward!
scala> trait Adder[T] { def add(t1: T, t2: T): T }
defined trait Adder
scala> def sum[T: Adder](ts: List[T]) = ts.reduce(implicitly[Adder[T]].add)
sum: [T](ts: List[T])(implicit evidence$1: Adder[T])T
looks the same as in Java, but but...
scala> case class Money(euro: Int)
defined class Money
scala> implicit object MoneyAdder extends Adder[Money] {
def add(t1: Money, t2: Money) = Money(t1.euro + t2.euro)
}
defined module MoneyAdder
scala> sum(List(Money(1), Money(2)))
res1: Money = Money(3)
def sum[T: Adder](ts: List[T]) = ts.reduce(implicitly[Adder[T]].add)
def sum[T](ts: List[T])(implicit e: Adder[T]) = ts.reduce(implicitly[Adder[T]].add)
def sum[T](ts: List[T])(implicit e: Adder[T]) = ts.reduce(e.add)
In Scala, the strategy is passed as a parameter just like in Java, but the compiler does it for you!
Trivial to use a custom implementation instead of the default.
In Scala, the compiler implicitly passes suitable objects to functions. Often we don't need to care since the standard library contains many default implicits.
In Haskell, we use imports and hiding to select visible type class instances.
Both have many sensible defaults.
we can select one of "type class instances" to be the "default instance" also in Java
class MoneyAdder implements Adder<Money> {
public Money add(Money t1, Money t2) { ... }
}
class AlternativeMoneyAdder implements Adder<Money> {
public Money add(Money t1, Money t2) { ... }
}
class Money implements Addable<Money> {
private static final DEFAULT_ADDER = new MoneyAdder();
public Money add(Money another) {
return DEFAULT_ADDER.add(this, another);
}
}
a more familiar example may be Comparator/Comparable
public interface Comparator<T> {
int compare(T o1, T o2);
}
public interface Comparable<T> {
int compareTo(T o);
}
(ever wondered why there's "two of them"?)
Comparator is kind of a
type class.
Each implements Comparator is like an instance of this type class.
Comparable is a way to pick a default instance from the Comparator type class.
many times there's no sensible default,
so better not to pick one.
class Employee {
String firstName;
String lastName;
}
class EmployeeComparator1 implements Comparator<Employee> {
int compare(T o1, T o2) { return o1.firstName.compareTo(o2.firstName); }
}
class EmployeeComparator2 implements Comparator<Employee> {
int compare(T o1, T o2) { return o1.lastName.compareTo(o2.lastName); }
}
Programming is like sex. One mistake and you have to support it for the rest of your life.
Michael Sinz
REPL! (or look down...)
Prelude> data Money = Money Int
Prelude> print (Money 42)
<interactive>:2:1:
No instance for (Show Money) arising from a use of `print'
Possible fix: add an instance declaration for (Show Money)
In the expression: print (Money 42)
In an equation for `it': it = print (Money 42)
Prelude> instance Show Money where show (Money a) = "I've got " ++ show a ++ "€"
Prelude> print (Money 42)
I've got 42€
do as you would do in Haskell
do as Dijkstra would approve


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University of California, US
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University of California, US
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University of California, US
204 Pages
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University of California, US
204 Pages
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University of California, US
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AI?
| think like humans |
think rationally |
| act like humans |
act rationally |
Turing's Test
A computer would deserve to be called intelligent if it could deceive a human into believing that it was human.
— Alan Turing
What does it take to create AI?
Human "senses" necessary for most task:
- Speaking / Hearing
- Seeing
- Experience / Knowledge
- Logical Deductions
- Motion (walking, lifting, ...)
Necessary "computational" properties
- Natural Language Processing (NLP)
- Computer Vision
- Knowledge Representation
- Automated Reasoning
- Machine Learning
- Robotics
in this talk
focus on Machine Learning
- What is ML and Pattern Recognition
- Supervised vs. Unsupervised
- (Deep) Neural Networks and Statistical Mechanics
- Modern Applications
What is Learning?
- Remembering
- Adapting
- Generalizing
Pattern Recognition
- easy for humans to recognize
- much harder for a computer!
(what rule would you write for that?)
Example: Cancer detection
Contest on Mitosis Detection, Jürgen Schmidhuber
Can also be used for detection of new features
ML as a fitting problem
| feature |
label |
| 0.1 |
1 |
| -0.3 |
0 |
| 0.2 |
1 |
| ... |
... |
What is the correct model?
Generalization is a guiding principle
Curse of dimensionality
- Einstein is $512 \times 512$ px
- Einstein is colorful
- Dimensionality: $512^2 \times 3 = 786432$!
Curse of dimensionality - cont'd
- $d$-dimensional unit ball:
$$ V(d) \sim \frac{\text{e}^{d}}{d^{d-1/2}} \stackrel{d\rightarrow\infty}{\longrightarrow} 0$$
- $\mathbb{x} \in \text{Unif}^d(0,1)$
- Most data lies outside unit ball!
- data becomes sparse
- difficult to detect patterns!
key concepts
- Not simple curve fitting, but pattern recognition
- Model selection
- Feature selection
- Generalizablity to "new data points"
Supervised learning
- Learning from labeled data: $$f_\theta(\mathbb{x_i}) = \hat y_i$$
- Loss function:$$L = \sum_i \ell(y_i, \hat y_i)$$
- Regularization: $$R(\theta) = -\lambda_1 |\theta| -\lambda_2 \theta^2$$
- Learning as Optimization problem:
$$\theta_\text{opt} = \text{min } [L(\theta) + R(\theta)]$$
Classification And Regression
- Regression:
- $\hat y \in \mathbb{R}^n$
- example: $\ell(y, \hat y) = (y - \hat y)^2$
- Classification:
- $y \in \{0,1\}$ and $\hat y = p(y = 1 | \mathbb{x}) \in [0,1]$
- log loss (cross-entropy): $\ell(y, \hat y) = y \ln\hat y - (1-y) \ln(1-\hat y)$
- example: $\hat y = \frac{1}{1+\text{exp} [-\theta^T\mathbb{x}]}$
(Some) Supervised ML Algos
- Logistic Regression
- Decision Tree
- Random Forest
- Support Vector Machine
- Naïve Bayes (for text data)
Applications in Physics
Dark Matter Mapping Challenge
Finding $\tau \rightarrow 3\mu$ decay in LHC data
Pilana et al., Scientific Reports 6, 19375
Unsupervised Learning
a.k.a. Clustering
- What happens if we have no labeled data?
- Labeling data is expensive (human, time, money)!
- outlier detection
- segmentation problems
Clustering cont'd
- Pre-processing to "generate labels"
- Visualization of high-dimensional data when combined with dimensionality reduction techniques
explosion of models and options
Recommender Engines
- How to recommend items to a new customer?
- Collaborative filtering (Factor analysis)
- E.g. Amazon, Netflix, Yelp, Foursquare, ...
- E.g. imputation of missing data
ImageNet Challenge
- ImageNet: $14,197,122$ images & $21,831$ categories
- Human classification error rate: $5\%$
The rise of Neural Networks
- Inspired by brain neurons
- Directed graph model
- Neurons
- connection matrix $W$
- activation $\varphi$
- Classifier function:
$$y_i = \varphi \big(W \mathbb{x}_i\big)$$
How do we train it?
- Optimization using gradient descent:
$$W_{ij}^{(t+1)} = W_{ij}^{(t)} - \eta \frac{\partial L^{(t)}}{\partial W_{ij}^{(t)}}$$
- Loss function in general not convex
- Optimization can get stuck in local minimum
- Good initialization of weights leads to better results.
Problems
- Simple NN is two step process
- Any function can be approximated
- Requires exponentially many nodes in hidden layer!
Solution - Go Deep!
- Add more layers: Deep NN
- $$ y_i = \varphi_N \circ \varphi_{N-1} \circ ... \circ \varphi_1 \big(W_1 \mathbb{x}_i\big)$$
- Training still hard & time-intensive: Back-propagation
- Technological advances helps mitigate this
Understanding the NN
- Linearly inseparable classes
- Layers learn non-linear combinations
Restricted Boltzmann machines
- Unsupervised version of a NN
- probability: $$ p(\mathbb{v},\mathbb{h}) = \text{e}^{-E(\mathbb{v},\mathbb{h})}/Z$$
- energy functional: $$E(\mathbb{v},\mathbb{h}) = -\mathbb{b}^T\mathbb{v} - \mathbb{c}^T\mathbb{h}- \mathbb{h}^TW\mathbb{v} $$
- Objective: Minimize negative Log-likelihood
- Training using Gibbs sampling and Contrastive Divergence
- empirically: Great initialization for supervised NN training
Connection to Quantum Physics
Why is this exciting?
Skype Instant Translator
Self Driving Cars
Biometrics
AlphaGo
RankBrain
Google Translate
many more
- Health Care
- Internet of Thing (IoT)
- Online Advertisement
- Automated Trading
- Online Auction Systems (PayPal, Ebay, ...)
- Just-In-Time production optimization
- ...
- AI is already surrounding us
- AI is powerful: huge potential & risk
- Don't be opposed, but keep an open mind
- Be deliberate with your data, but not protective


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My name is Lauren Winters. I have always been passionate about art and design. That passion has given me the knowledge to solve traditional problems in non-traditional creative ways. I am a self-driven and resourceful person with commitment to producing quality work. I'm continuously looking to build my skills. I seek a job that will challenge my creativity and skills as a designer.


Version 0.95-0
-
- Updated for R-4.2.0
- PROBLEM, ERROR, WARNING macros.
Version 0.94-0
-
- The names() method for a ZipArchive object now checks if the
archive on disk has been updated more recently than the original
readTime and if so, doesn't use the cached names.
- We may add an additional class to allow for a "const" or
non-volatile zip archive that the caller knows will not be updated.
Version 0.93-2
- Added unistd.h as an include in untgz.h for the Mac as it was
needed on some/at least one OS X configurations. Thanks to
Stephen Eglen.
-
Version 0.93-1
- configure script was not built.
-
Version 0.93-0
-
- Changed the license to zlib as some of the code was under that license.
- Thanks to Pierre-Yves Chibon for bringing this to my attention
as he made this R package it into a package for Fedora.
Version 0.92-0
-
- Replacing elements in an RdzArchive file updates the .toc.
-
-
- Replacing elements in an archive now feasible.
-
-
- Support for comments on entries in a zip file, both reading
them and writing them during the call to zip(). Including a
global comment on the file. (getZipComment() for the global
comment and getZipInfo().)
-
-
- zipArchive("name")[ ] returns a list of giving the contents
of all the elements of the archive and works faster by
reading the sequentially and continuing from the last point
read (as opposed to read each one separately and having to traverse
the archive each time).
-
-
- RdzArchive$name = value appends value to the RdzArchive.
-
-
- Appending to an archive done via in-memory operations rather
than call to external zip executable and unzipping and copying files
to temporary directory.
-
-
- Time stamp in zip files created in R with in-memory objects
as entries use the current time - Sys.time().
-
Version 0.91-0
-
- Added a zave() function (concatenation of zip and save) to serialize a collection of R objects to
a zip archive. These are the accessible individually, rather than having to restore
all the variables in the archive. Also, one can inquire about the variables in the
file using
names(rdz)
and even determine the class of the objects via a
table of contents.
-
-
- Added RdzArchive class to represent an R object serialized object.
This provides methods for un/de-serializing objects in the archive.
-
-
- zip() can handle in-memory content, i.e. raw and AsIs character vectors,
along with file names (all in the same call).
-
-
- Fixed getZipFileEntry to handle mode = "raw" or mode = "binary".
-
-
- Fixed the [[ method for ZipArchive to return raw vectors correctly
-
Version 0.9-0
-
- Added a zip() function to create a zip() file in memory.
- It handles appending files, but one can end up with duplicates
and we cannot remove or replace files in the current implementation.
Version 0.7-0
-
- Added resizeBy parameter to uncompress() which controls
how the buffer size is enlarged when more memory is needed
for the uncompressed data.
-
-
- Check for user interrupts and events in uncompress().
-
Version 0.6-0
-
- Added ability to work with Zip archives directly in memory.
- This allows one to download a Zip file via getURLContent() in
the RCurl package and then work with that without writing the
contents to disk. It also allows one to take a snapshot of the contents of a Zip file
and work with that without fear of it being modified by other
applications, etc.
Version 0.5-0
-
- Facilities for extracting elements of a ZIP archive, even with
a password. zipArchive() and methods for accessing elements of
a ZIP archive as if it were a list.
-
-
- Truncated file names in getZipInfo() now
corrected. filename.size parameter added.
-
Version 0.4-0
-
- Support for reading from a RAW vector rather than a file in
tarInfo() and tarExtract().
-
-
- Documentation for and minor changes to collectContents().
-
-
- Handle includes on the Mac where direct.h and io.h are not
available or needed.
-
Version 0.3-1
- compress() now sets the length of the resulting vector
correctly, rather than leaves the trailing elements of the
working vector as '0'. So the length of the result
is the number of bytes of the compressed data.
This can be used to measure the compression ratio,
e.g.
length(compress(txt))/nchar(txt)
.
-
Version 0.3-0
-
- Connected the code for dealing with gzipped tar files
(.tar.gz/.tgz). The functions to use are tarInfo() and tarExtract().
-
Version 0.2-1
-
- Removed dependency on RGCCTranslationUnit package. We include
the relevant code in this package.
-
Version 0.2-0
-
- Support for getting "table of contents" information from a zip file.
-
Duncan Temple Lang
<EMAIL_ADDRESS>Last modified: Wed Jan 18 18:36:23 PST 2012


From quick research, I found that there are a lot of mathematical approaches to create a maze and to solve it. I decided for practice to ignore all the theories and use a random approach.
Create a maze
Dimensions: NxN ;
1 entry ;
n exits ;
import numpy as np
import time
import functools
from pprint import pprint
from random import randint, choice, random
DIM=10
def timer(func):
@functools.wraps(func)
def wrapper_timer(*args, **kwargs):
start_time = time.perf_counter()
value = func(*args, **kwargs)
end_time = time.perf_counter()
run_time = end_time - start_time
print(f"Finished {func.__name__!r} in {run_time:.4f} secs")
return value
return wrapper_timer
class Maze:
def __init__(self, dim, number_of_exits):
self._m = np.full((dim, dim), '#', dtype=str)
self._dim = dim
self.entry = (0, randint(1,dim-1))
self.number_of_exits = number_of_exits
def in_bound(self, x, y, incx, incy):
return (x+incx > 0 and \
x+incx <= self._dim-1 and \
y+incy <= self._dim-2 and \
y+incy>0)
def is_validcell(self, x, y):
return self._m[x][y] == '0'
@timer
def create(self):
coord = [-1, 0, 1]
for i in range(self.number_of_exits):
x = self.entry[0]
y = self.entry[1]
self._m[x][y] = '0'
while x != self._dim-1:
#main path
incx = 1
incy = choice(coord)
if self.in_bound(x, y, incx, incy):
x += incx
y += incy
self._m[x][y] = '0'
if random() > 0.6:
#branching paths
xx = x
yy = y
for _ in range(int(0.7*self._dim)):
incxx = choice(coord)
incyy = 1
if self.in_bound(xx, yy, incxx, incyy ):
xx += incxx
yy += incyy
if(xx < self._dim-1):
self._m[xx][yy] = '0'
@timer
def solve(self):
path = []
cost = 0
coord = [-1, 0, 1]
x, y = self.entry[0], self.entry[1]
while x != self._dim-1:
incx = choice(coord)
incy = choice(coord)
if self.in_bound(x, y, incx, incy) and \
self.is_validcell(x+incx,y+incy):
x += incx
y += incy
print((x,y))
cost += 1
print("cost: {}".format( cost))
return cost, path
def __str__(self):
return '{}'.format(self._m)
def __repr__(self):
return '{}'.format(self._m)
A Sample output from the create method.
‘0’ represents clear path. ‘#’ represents a wall.
Solve the maze
It is expected to find an exit. The entry is placed North, whilst the exits are South. The objective could have been finding the minimal path cost. The deck of possible algorithms is wide, from genetic algorithms, ACO, binary search, etc.
Upon solving the problem the algorithm generates random coordinates that go back and forth till finding a cell in the last row of the matrix. And that’s all!
Sources:
- https://theconversation.com/how-to-escape-a-maze-according-to-maths-71582
- http://www.astrolog.org/labyrnth/algrithm.htm
- https://www.cs.bu.edu/teaching/alg/maze/
- https://github.com/theJollySin/mazelib/tree/master/mazelib
- https://stackoverflow.com/questions/38502/whats-a-good-algorithm-to-generate-a-maze
- http://weblog.jamisbuck.org/2011/2/7/maze-generation-algorithm-recap


Order complete
When a designer has processed your order, you can use the download
link below to get the .prt file with weld lines. You will also get an
email with a downlaod link.
Click to downlaod PRT file


Please arrive at {% if include.service == "morning" %}08:45{% else %}15:45{% endif %} to open the Bible and pray together, set up and practise.
Bible passage:


Preheat the oven to 150C/300F/Gas 2. Break up the digestive biscuits and the ginger biscuits and place them into the bowl of a food processor.
Place the butter in a heavy-based pan and melt gently.
Process the biscuits in the food processor, until the mixture has the consistency of fine breadcrumbs.
Line the base of a 24cm/9in-10in loose-based spring-form cake tin with a round of greaseproof paper. Brush the bottom of the tin with some of the melted butter and place the round of greaseproof paper in the base.
Add the remaining melted butter to the biscuit crumbs in the food processor and process again briefly.
Tip the crumbs into the bottom of the cake tin. Using the back of a spoon, gently push the crumbs from the centre outward, until smooth and level. This will form the base of the cheesecake.
Bake the cheesecake base in the oven for 10-15 minutes until it's lightly browned and just set. Remove from the oven and allow to cool for at least five minutes.
Meanwhile, make the filling. Place the cream cheese, cream, sugar and eggs in a large bowl. Using a grater, grate in the zest of the limes. Using an electric whisk, beat together on a low speed, until smooth.
Carefully pour the filling onto the cooled base. If making the cheesecake in advance, you can prepare it to this stage, then chill in the refrigerator and bake the next day (allowing it to first reach room temperature).
Return the cheesecake to the oven and bake for about one hour, or until just set. Allow to cool completely, then place in the fridge and chill for about an hour.
Carefully remove the cheesecake from the tin and place on a serving plate, using a palette knife to loosen the cake base from the bottom of the tin.
For the topping, use a sharp serrated knife to remove the top and bottom of the pineapple. Cut away the tough outer skin from the pineapple and cut in half lengthways. Using a mandoline, thinly slice half of the pineapple (be sure to use the finger guard if the mandoline has one). Reserve the other half for use in another dish. Place the pineapple slices into a bowl.
Cut the passion fruit in half and scoop out the pulp with a teaspoon. Add to the bowl with the pineapple and toss together well.
Whip the cream in a clean bowl with a hand whisk. When soft peaks form, add icing sugar, to taste, and whisk gently until well blended.
To serve, spread the sweetened cream over the surface of the cheesecake and pile on the fresh fruit. Pour over the passion fruit juice from the bowl. Dust with icing sugar and serve in generous slices.


What is BioStudies?
The BioStudies database holds descriptions of biological studies, links to data from these studies in other databases at EMBL-EBI or outside, as well as data that do not fit in the structured archives at EMBL-EBI. The database accepts submissions via an online tool, or in a simple tab-delimited format. It also enables authors to submit supplementary information and link to it from the publication. See McEntyre J. et al., 2015. The BioStudies database.
Publications / How to cite
- Citing BioStudies database: please use the following publication: McEntyre J. et al., 2015. The BioStudies database.
- Citing BioStudies submission in your manuscript: please include your experiment accession number and the URL to BioStudies home page, http://www.ebi.ac.uk/biostudies. e.g. "Data are available in the BioStudies database (http://www.ebi.ac.uk/biostudies) under accession number S-EPMC5090800."
The team behind BioStudies
The BioStudies team is part of the Ugis Sarkans's team at EMBL-EBI.
The BioStudies team would like to thank the following companies for their support through the offering of free software licenses:
- JetBrains for the most intelligent Java IDE - IntelliJ IDEA.
- Atlassian for the Bamboo - Continuous Integration and Release Management tool.
- SpryMedia for the DataTables jQuery plugin.


public class StatList
extends java.lang.Object
| Modifier and Type | Field and Description |
|---|---|
static java.util.List |
allStats |
static StatBase |
damageDealtStat
the amount of damage you've dealt
|
static StatBase |
damageTakenStat
the amount of damage you have taken
|
static StatBase |
deathsStat
the number of times you have died
|
static StatBase |
distanceByBoatStat
the distance you've traveled by boat
|
static StatBase |
distanceByMinecartStat
the distance you've traveled by minecart
|
static StatBase |
distanceByPigStat
the distance you've traveled by pig
|
static StatBase |
distanceClimbedStat
the distance you've climbed
|
static StatBase |
distanceDoveStat
the distance you've dived
|
static StatBase |
distanceFallenStat
the distance you have fallen
|
static StatBase |
distanceFlownStat
the distance you've flown
|
static StatBase |
distanceSwumStat
distance you have swam
|
static StatBase |
distanceWalkedStat
distance you've walked
|
static StatBase |
dropStat
the distance you've dropped (or times you've fallen?)
|
static StatBase |
field_151183_A |
static StatBase |
field_151184_B |
static StatBase |
field_151185_q |
static StatBase |
field_151186_x |
static StatBase |
fishCaughtStat |
static java.util.List |
generalStats |
static java.util.List |
itemStats |
static StatBase |
jumpStat
the times you've jumped
|
static StatBase |
leaveGameStat
number of times you've left a game
|
static StatBase[] |
mineBlockStatArray |
static StatBase |
minutesPlayedStat
number of minutes you have played
|
static StatBase |
mobKillsStat
the number of mobs you have killed
|
static StatBase[] |
objectBreakStats
Tracks the number of times a given block or item has been broken.
|
static StatBase[] |
objectCraftStats
Tracks the number of items a given block or item has been crafted.
|
static java.util.List |
objectMineStats
Tracks the number of times a given block or item has been mined.
|
static StatBase[] |
objectUseStats
Tracks the number of times a given block or item has been used.
|
protected static java.util.Map |
oneShotStats
Tracks one-off stats.
|
static StatBase |
playerKillsStat
counts the number of times you've killed a player
|
| Constructor and Description |
|---|
StatList() |
| Modifier and Type | Method and Description |
|---|---|
static StatBase |
func_151176_b(EntityList.EntityEggInfo p_151176_0_) |
static StatBase |
func_151177_a(java.lang.String p_151177_0_) |
static void |
func_151178_a() |
static StatBase |
func_151182_a(EntityList.EntityEggInfo p_151182_0_) |
protected static java.util.Map oneShotStats
public static java.util.List allStats
public static java.util.List generalStats
public static java.util.List itemStats
public static java.util.List objectMineStats
public static StatBase leaveGameStat
public static StatBase minutesPlayedStat
public static StatBase distanceWalkedStat
public static StatBase distanceSwumStat
public static StatBase distanceFallenStat
public static StatBase distanceClimbedStat
public static StatBase distanceFlownStat
public static StatBase distanceDoveStat
public static StatBase distanceByMinecartStat
public static StatBase distanceByBoatStat
public static StatBase distanceByPigStat
public static StatBase field_151185_q
public static StatBase jumpStat
public static StatBase dropStat
public static StatBase damageDealtStat
public static StatBase damageTakenStat
public static StatBase deathsStat
public static StatBase mobKillsStat
public static StatBase field_151186_x
public static StatBase playerKillsStat
public static StatBase fishCaughtStat
public static StatBase field_151183_A
public static StatBase field_151184_B
public static final StatBase[] mineBlockStatArray
public static final StatBase[] objectCraftStats
public static final StatBase[] objectUseStats
public static final StatBase[] objectBreakStats
public static void func_151178_a()
public static StatBase func_151182_a(EntityList.EntityEggInfo p_151182_0_)
public static StatBase func_151176_b(EntityList.EntityEggInfo p_151176_0_)
public static StatBase func_151177_a(java.lang.String p_151177_0_)


America's Declining Happiness - A social or an economic crisis?
What is Happiness Score?
Happiness Index Score measures the sustainable well-being for a country. It is an indication which tells us how well a nation is doing at achieving long, happy, sustainable lives. It is important to measure the happiness index scorebecause it is a better measure of a nation’s progress than individual measures like GDP, Life
Expectancy etc. Happiness Index Score is calculated taking factors like GDP per Capita, Family happiness, Life Expectancy, Freedom, Generosity, Trust etc. Taking these factors into consideration,
we are analyzing how the happiness index score of USA increases or decreases due to the above factors.
Click on the buttons on the right to see the top 10 happy/sad countries
Happiest Countries
Saddest Countries
Story Premise
For the second consecutive year in 2018, the United States has taken a notch down in the World Happiness Report’s annual ranking of more than 150 countries, published by the Sustainable Development Solutions Network.
It is a general belief that a country with a booming economy and high income should have a high happiness rank.
Comparison of Rate of change of GDP and Happiness for
Cameroon
and
USA
for years 2015-2017
We can see in the graphs above that the happiness score for the USA has continuously dropped. Even though the happiness score has taken a dip, over the last 3 years, the GDP has been increasing. Over the last half-century, the GDP of the US has more than tripled. But the same is not true for Cameroon, whose happiness and GDP are increasing hand in hand.
So what’s causing USA’s happiness to decline?.
“rising inequality, corruption, isolation, and distrust."
1. Trends in Drug abuse Percentage
Latin America
and
USA
for years 2014-2016
1. Increase in substance abuse in America is a clear indicator of the collective mental state of Americans. Surveys have shown that American workers are struggling with high levels of stress anxiety and depression and thus lower happiness.
2. But are there other factors at play? When you compare Latin America with the USA, the results are surprising.Both these regions have a long and grave drug abuse problem, yet Costa-Rica (a country in Latin America) has a happiness score greater than that of the USA.
This can be explained by the abundance of family warmth and other supportive social relationships in Latin America. Having someone to count on has a big impact on happiness, and the quality of the community is key to feeling safe. 2017 was a challenging year for Americans’ well-being. The national Well-Being Index score for the U.S. in 2017 was 61.5 – a decline from 62.1 in 2016.
2. Trends in World Giving (Generosity) Index rank for USA for the years 2014-2017
1.Generosity is measured by World giving Index. The United States has slipped down the rankings to fifth place, its lowest ranking since 2011. A five percentage point decrease in the USA’s overall score is driven by lower levels of donating money (down seven percentage points to 56%) and volunteering time (down five percentage points to 41%).
Happier countries tend to be more generous and think about extending advantages to future generations and other countries. And engaging in some sort of charity and donations makes one feel grateful and is one of the key elements for feeling satisfied and happy.
3. Income inequality in USA for the year 2015
There is huge income inequality in the USA as seen in the graph below.
As the incomes of the 1% pull away from those of the rest, people’s overall life satisfaction becomes lower and their day-to-day negative emotional experiences are greater in number. This can be explained by a simple reason that even if you are a member of the relatively well-to-do middle class, some things start getting priced beyond your reach, such as private schools and houses in the best neighborhoods.
4. Trends in Corruption Perception Index
Nearly six in ten people now say that the level of corruption has risen in the past twelve months, up from around a third who said the same in January 2015.
Corruption is measured by The Corruption Perceptions Index (CPI) published annually by Transparency International since 1995 which ranks countries "on a scale from 100 (very clean) to 0 (highly corrupt)". Corrupt nations tend to have low levels of happiness and life satisfaction. More and more people are losing their trust int he government bodies due to corruption which in turn making them unhappy.
Conclusion
As seen from the graphs above, to make America happier again, we need to come up with policies that target social factors like curbing corruption, effective drug control, improving social support system and bridging the huge income gap between the riches and the poor. Simply focusing on increasing the wealth and economy won’t help, it’s the proper distribution of the wealth that can make Americans believe that the government is working for them rather than just for the rich.


toast.js Examples
Project home on
Github
.
Success
Sticky success with title
Sticky info
Info: Change displayDuration to 200ms
Warning: Change displayDuration to 2000ms
Warning with title
Error
Error with title
Info with title: Changed default width to 600px
Info with title: Changed default width to 200px


| 19 Aug 1953 |
The CIA and British intelligence orchestrate the overthrow of the democratically elected government of Iran. |
| 4 Nov 1979 |
Iranian students storm the U.S. Embassy, seizing 52 hostages and occupying the embassy for 444 days. |
| 20 Jan 1981 |
The American hostages in Teheran are released immediately after Ronald Reagan is inaugurated as U.S. President. |
| 3 Jul 1988 |
U.S. battleship Vincennes shoots down Iran Air flight 655 by accident, killing 290 civilian passengers and crew. |


| After his engagement ends badly, Josh decides to take advantage of his bachelor-party plans in Ojai, California, with the few friends still willing to join him. Focused on drugs and their own hangups, his self-absorbed friends refuse to confront the elephant in the room and ask Josh how he’s feeling. As welcome and unwelcome guests stop by, Josh will attempt to find some closure over this weekend with the guys. | |
| Genres: | Comedy |
| Director: | Jeff Baena |
| Writer: | Jeff Baena |
Cast: | Josh - Thomas Middleditch Ari - Adam Pally Adam - Alex Ross Perry Eric - Nick Kroll Greg - Brett Gelman Jodi - Jenny Slate Katee - Lauren Graham Jen - Aubrey Plaza Aaron - Joe Swanberg Anita - Kris Swanberg Rachel - Alison Brie Private Investigator - Paul Weitz Tonie - Brian Patrick Farrell Reggie - Jake Johnson Crystal - Frankie Shaw Kylie - Jess Varley The Spaniard - Luis Fernandez-Gil Isadora - Lauren Weedman Steve - Paul Reiser Claudia - Lisa Edelstein Shauna - Samantha Jacober Ali - Jenn Schatz Jude - Jude Swanberg |


| Home | Trees | Indices | Help |
|
|---|
|
|
Propagator class employing stepwise trajectories as used in the NCMC algorithm (Nilmeier et al., "Nonequilibrium candidate Monte Carlo is an efficient tool for equilibrium simulation", PNAS 2011)
| Classes | |
|
AbstractHeatWorkJacobianLogger Abstract class defining the interface for objects keeping track of and accumulating heat, work and the Jacobian during a nonequilibrium trajectory. |
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AbstractMCPropagation System propagation by some MC algorithm. |
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AbstractMDPropagation System propagation by some MD algorithm |
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AbstractPerturbation Describes an abstract system perturbation |
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AbstractPerturbationParam Subclasses hold informations required for different kinds of system perturbation |
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AbstractPropagation Describes an abstract system propagation |
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AbstractPropagationParam Subclasses hold informations required for different kinds of system propagation |
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AbstractStepStatistics Abstract class defining a minimal interface for objects allowing to store statistics of what happens in Step instances. |
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AbstractSystemInfo Subclasses hold all information describing a current system setup (Hamiltonian, boundaries, ...) |
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| DummyStepStatistics | |
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HMCPropagation System propagation by HMC |
|
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HMCPropagationParam Holds all required information for propagating a system by HMC. |
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HamiltonianSysInfo Holds information describing a system by its Hamiltonian only. |
|
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MDParam Holds all required information for calculating a MD trajectory. |
|
| MDPropagationParam | |
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NonequilibriumStepPropagator Propagator class which propagates a system using NCMC-like stepwise trajectories |
|
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NonequilibriumTrajectory Trajectory holding additional information about energy difference the Jacobian. |
|
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PerturbationResult Instances hold the result of a perturbation. |
|
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PlainMDPropagation System propagation by plain, microcanonical MD |
|
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PlainMDPropagationParam Holds all required information for propagating a system by MD. |
|
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Protocol Describes a stepwise protocol as in Nilmeier et al. |
|
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ReducedHamiltonian Describes a reduced Hamiltonian (Hamiltonian, its position gradient and the system temperature) |
|
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ReducedHamiltonianPerturbation System perturbation by changing the reduced Hamiltonian |
|
|
Step Defines a step in an NCMC-like stepwise protocol. |
|
| TrivialHeatWorkJacobianLogger |
| Variables | |
__package__ =
|
| Home | Trees | Indices | Help |
|
|---|
| Generated by Epydoc 3.0.1 on Tue Jul 4 20:19:04 2017 | http://epydoc.sourceforge.net |


Israel says intercepts rockets fired from Syria
JERUSALEM (Reuters) - Israel’s military said its missile defense system shot down four rockets fired from Syria toward Israel early on Tuesday.
Minutes earlier, sirens warning of incoming rocket fire sounded in the area of the Israeli-occupied Golan Heights.
“Four launches were identified from Syria towards Israeli territory which were intercepted by the Israeli air defense systems,” the army said. “No hits on Israeli communities were identified.”
Israel captured the Golan from Syria in a 1967 war and later annexed and settled it - moves not accepted by most world powers.
Israel says it needs to keep the plateau as a buffer zone between Israeli towns and the instability of its neighbor to the north. Syria insists that the part of the Golan held by Israel is occupied territory and has demanded its return.


| Title | Using stochastic physics to repesent model uncertainty |
| Publication Type | Education material |
| Date Published | 2018 |
| Secondary Title | Numerical weather prediction and ECMWF products |
| Pagination/Duration | 1 hour |
| Author | Lock, S-J |
| Event Series/Collection | ECMWF eLearning online resources |
| Publisher | ECMWF |
| Keywords | ensemble, forecast, Uncertainty |
| URL | https://www.ecmwf.int/node/19275 |


COVID-19 Positive Testing Rates
Data Source: JHU
As of 2020-09-16
USA (by date, by state)
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