text:
================================================================================
pyramid-excel - Let you focus on data, instead of file formats
================================================================================

.. image:: https://raw.githubusercontent.com/pyexcel/pyexcel.github.io/master/images/patreon.png
   :target: https://www.patreon.com/chfw

.. image:: https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg
   :target: https://awesome-python.com/#specific-formats-processing

.. image:: https://travis-ci.org/pyexcel-webwares/pyramid-excel.svg?branch=master
   :target: http://travis-ci.org/pyexcel-webwares/pyramid-excel

.. image:: https://codecov.io/gh/pyexcel-webwares/pyramid-excel/branch/master/graph/badge.svg
   :target: https://codecov.io/gh/pyexcel-webwares/pyramid-excel

.. image:: https://badge.fury.io/py/pyramid-excel.svg
   :target: https://pypi.org/project/pyramid-excel

.. image:: https://pepy.tech/badge/pyramid-excel/month
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   :target: https://gitter.im/pyexcel/Lobby

.. image:: https://img.shields.io/static/v1?label=continuous%20templating&message=%E6%A8%A1%E7%89%88%E6%9B%B4%E6%96%B0&color=blue&style=flat-square
    :target: https://moban.readthedocs.io/en/latest/#at-scale-continous-templating-for-open-source-projects

.. image:: https://img.shields.io/static/v1?label=coding%20style&message=black&color=black&style=flat-square
    :target: https://github.com/psf/black
.. image:: https://readthedocs.org/projects/pyramid-excel/badge/?version=latest
   :target: http://pyramid-excel.readthedocs.org/en/latest/

Support the project
================================================================================

If your company has embedded pyexcel and its components into a revenue generating
product, please support me on github, `patreon <https://www.patreon.com/bePatron?u=5537627>`_
or `bounty source <https://salt.bountysource.com/teams/chfw-pyexcel>`_ to maintain
the project and develop it further.

If you are an individual, you are welcome to support me too and for however long
you feel like. As my backer, you will receive
`early access to pyexcel related contents <https://www.patreon.com/pyexcel/posts>`_.

And your issues will get prioritized if you would like to become my patreon as `pyexcel pro user`.

With your financial support, I will be able to invest
a little bit more time in coding, documentation and writing interesting posts.

Known constraints
==================

Fonts, colors and charts are not supported.

Introduction
================================================================================
Here is a typical conversation between the developer and the user::

 User: "I have uploaded an excel file"
       "but your application says un-supported file format"
 Developer: "Did you upload an xlsx file or a csv file?"
 User: "Well, I am not sure. I saved the data using "
       "Microsoft Excel. Surely, it must be in an excel format."
 Developer: "OK. Here is the thing. I were not told to support"
            "all available excel formats in day 1. Live with it"
            "or delay the project x number of days."

**pyramid-excel** is based on `pyexcel <https://github.com/pyexcel/pyexcel>`_ and makes
it easy to consume/produce information stored in excel files over HTTP protocol as
well as on file system. This library can turn the excel data into a list of lists,
a list of records(dictionaries), dictionaries of lists. And vice versa. Hence it
lets you focus on data in Pyramid based web development, instead of file formats.

The idea originated from the common usability problem: when an excel file
driven web application is delivered for non-developer users (ie: team assistant,
human resource administrator etc). The fact is that not everyone knows (or cares)
about the differences between various excel formats: csv, xls, xlsx are all
the same to them. Instead of training those users about file formats, this
library helps web developers to handle most of the excel file
formats by providing a common programming interface. To add a specific excel
file format type to you application, all you need is to install an extra pyexcel
plugin. Hence no code changes to your application and no issues with excel file
formats any more. Looking at the community, this library and its associated ones
try to become a small and easy to install alternative to Pandas.

The highlighted features are:

#. excel data import into and export from databases
#. turn uploaded excel file directly into Python data structure
#. pass Python data structures as an excel file download
#. provide data persistence as an excel file in server side
#. supports csv, tsv, csvz, tsvz by default and other formats are supported via
   the following plugins:

.. _file-format-list:
.. _a-map-of-plugins-and-file-formats:

.. table:: A list of file formats supported by external plugins

   ======================== ======================= =================
   Package name              Supported file formats  Dependencies
   ======================== ======================= =================
   `pyexcel-io`_            csv, csvz [#f1]_, tsv,
                            tsvz [#f2]_
   `pyexcel-xls`_           xls, xlsx(read only),   `xlrd`_,
                            xlsm(read only)         `xlwt`_
   `pyexcel-xlsx`_          xlsx                    `openpyxl`_
   `pyexcel-ods3`_          ods                     `pyexcel-ezodf`_,
                                                    lxml
   `pyexcel-ods`_           ods                     `odfpy`_
   ======================== ======================= =================

.. table:: Dedicated file reader and writers

   ======================== ======================= =================
   Package name              Supported file formats  Dependencies
   ======================== ======================= =================
   `pyexcel-xlsxw`_         xlsx(write only)        `XlsxWriter`_
   `pyexcel-libxlsxw`_      xlsx(write only)        `libxlsxwriter`_
   `pyexcel-xlsxr`_         xlsx(read only)         lxml
   `pyexcel-xlsbr`_         xlsb(read only)         pyxlsb
   `pyexcel-odsr`_          read only for ods, fods lxml
   `pyexcel-odsw`_          write only for ods      loxun
   `pyexcel-htmlr`_         html(read only)         lxml,html5lib
   `pyexcel-pdfr`_          pdf(read only)          camelot
   ======================== ======================= =================

Plugin shopping guide
------------------------

Since 2020, all pyexcel-io plugins have dropped the support for python version
lower than 3.6. If you want to use any python verions, please use pyexcel-io
and its plugins version lower than 0.6.0.

Except csv files, xls, xlsx and ods files are a zip of a folder containing a lot of
xml files

The dedicated readers for excel files can stream read

In order to manage the list of plugins installed, you need to use pip to add or remove
a plugin. When you use virtualenv, you can have different plugins per virtual
environment. In the situation where you have multiple plugins that does the same thing
in your environment, you need to tell pyexcel which plugin to use per function call.
For example, pyexcel-ods and pyexcel-odsr, and you want to get_array to use pyexcel-odsr.
You need to append get_array(..., library='pyexcel-odsr').

.. _pyexcel-io: https://github.com/pyexcel/pyexcel-io
.. _pyexcel-xls: https://github.com/pyexcel/pyexcel-xls
.. _pyexcel-xlsx: https://github.com/pyexcel/pyexcel-xlsx
.. _pyexcel-ods: https://github.com/pyexcel/pyexcel-ods
.. _pyexcel-ods3: https://github.com/pyexcel/pyexcel-ods3
.. _pyexcel-odsr: https://github.com/pyexcel/pyexcel-odsr
.. _pyexcel-odsw: https://github.com/pyexcel/pyexcel-odsw
.. _pyexcel-pdfr: https://github.com/pyexcel/pyexcel-pdfr

.. _pyexcel-xlsxw: https://github.com/pyexcel/pyexcel-xlsxw
.. _pyexcel-libxlsxw: https://github.com/pyexcel/pyexcel-libxlsxw
.. _pyexcel-xlsxr: https://github.com/pyexcel/pyexcel-xlsxr
.. _pyexcel-xlsbr: https://github.com/pyexcel/pyexcel-xlsbr
.. _pyexcel-htmlr: https://github.com/pyexcel/pyexcel-htmlr

.. _xlrd: https://github.com/python-excel/xlrd
.. _xlwt: https://github.com/python-excel/xlwt
.. _openpyxl: https://bitbucket.org/openpyxl/openpyxl
.. _XlsxWriter: https://github.com/jmcnamara/XlsxWriter
.. _pyexcel-ezodf: https://github.com/pyexcel/pyexcel-ezodf
.. _odfpy: https://github.com/eea/odfpy
.. _libxlsxwriter: http://libxlsxwriter.github.io/getting_started.html

.. table:: Other data renderers

   ======================== ======================= ================= ==================
   Package name              Supported file formats  Dependencies     Python versions
   ======================== ======================= ================= ==================
   `pyexcel-text`_          write only:rst,         `tabulate`_       2.6, 2.7, 3.3, 3.4
                            mediawiki, html,                          3.5, 3.6, pypy
                            latex, grid, pipe,
                            orgtbl, plain simple
                            read only: ndjson
                            r/w: json
   `pyexcel-handsontable`_  handsontable in html    `handsontable`_   same as above
   `pyexcel-pygal`_         svg chart               `pygal`_          2.7, 3.3, 3.4, 3.5
                                                                      3.6, pypy
   `pyexcel-sortable`_      sortable table in html  `csvtotable`_     same as above
   `pyexcel-gantt`_         gantt chart in html     `frappe-gantt`_   except pypy, same
                                                                      as above
   ======================== ======================= ================= ==================

.. _pyexcel-text: https://github.com/pyexcel/pyexcel-text
.. _tabulate: https://bitbucket.org/astanin/python-tabulate
.. _pyexcel-handsontable: https://github.com/pyexcel/pyexcel-handsontable
.. _handsontable: https://cdnjs.com/libraries/handsontable
.. _pyexcel-pygal: https://github.com/pyexcel/pyexcel-chart
.. _pygal: https://github.com/Kozea/pygal
.. _pyexcel-matplotlib: https://github.com/pyexcel/pyexcel-matplotlib
.. _matplotlib: https://matplotlib.org
.. _pyexcel-sortable: https://github.com/pyexcel/pyexcel-sortable
.. _csvtotable: https://github.com/vividvilla/csvtotable
.. _pyexcel-gantt: https://github.com/pyexcel/pyexcel-gantt
.. _frappe-gantt: https://github.com/frappe/gantt

.. rubric:: Footnotes

.. [#f1] zipped csv file
.. [#f2] zipped tsv file

This library makes information processing involving various excel files as easy as
processing array, dictionary when processing file upload/download, data import into
and export from SQL databases, information analysis and persistence. It uses
**pyexcel** and its plugins:

#. to provide one uniform programming interface to handle csv, tsv, xls, xlsx, xlsm and ods formats.
#. to provide one-stop utility to import the data in uploaded file into a database and to export tables in a database as excel files for file download.
#. to provide the same interface for information persistence at server side: saving a uploaded excel file to and loading a saved excel file from file system.

Tested Pyramid Versions
========================

So far pyexcel-xls plugin does not work with Python 3.9

.. image:: https://img.shields.io/badge/pyramid-1.10.4-green.svg
    :target: http://travis-ci.org/pyexcel/pyramid-excel

.. image:: https://img.shields.io/badge/pyramid-1.9-green.svg
    :target: http://travis-ci.org/pyexcel/pyramid-excel

.. image:: https://img.shields.io/badge/pyramid-1.8.4-green.svg
    :target: http://travis-ci.org/pyexcel/pyramid-excel

.. image:: https://img.shields.io/badge/pyramid-1.7.6-green.svg
    :target: http://travis-ci.org/pyexcel/pyramid-excel

.. image:: https://img.shields.io/badge/pyramid-1.6.1-green.svg
    :target: http://travis-ci.org/pyexcel/pyramid-excel

.. image:: https://img.shields.io/badge/pyramid-1.5.8-green.svg
    :target: http://travis-ci.org/pyexcel/pyramid-excel

.. image:: https://img.shields.io/badge/pyramid-1.4.9-green.svg
    :target: http://travis-ci.org/pyexcel/pyramid-excel

Installation
================================================================================

You can install pyramid-excel via pip:

.. code-block:: bash

    $ pip install pyramid-excel

or clone it and install it:

.. code-block:: bash

    $ git clone https://github.com/pyexcel-webwares/pyramid-excel.git
    $ cd pyramid-excel
    $ python setup.py install

Setup
======
Once the pyramid_excel is installed, you must use the config.include mechanism to include
it into your Pyramid project's configuration::

    config = Configurator(.....)
    config.include('pyramid_excel')

Alternately, you may activate the extension by changing your application's .ini file by
adding it to the pyramid.includes list::

    pyramid.includes = pyramid_excel

Development guide
================================================================================

Development steps for code changes

#. git clone https://github.com/pyexcel/pyramid-excel.git
#. cd pyramid-excel

Upgrade your setup tools and pip. They are needed for development and testing only:

#. pip install --upgrade setuptools pip

Then install relevant development requirements:

#. pip install -r rnd_requirements.txt # if such a file exists
#. pip install -r requirements.txt
#. pip install -r tests/requirements.txt

Once you have finished your changes, please provide test case(s), relevant documentation
and update CHANGELOG.rst.

.. note::

    As to rnd_requirements.txt, usually, it is created when a dependent
    library is not released. Once the dependecy is installed
    (will be released), the future
    version of the dependency in the requirements.txt will be valid.

How to test your contribution
------------------------------

Although `nose` and `doctest` are both used in code testing, it is adviable that unit tests are put in tests. `doctest` is incorporated only to make sure the code examples in documentation remain valid across different development releases.

On Linux/Unix systems, please launch your tests like this::

    $ make

On Windows systems, please issue this command::

    > test.bat

Before you commit
------------------------------

Please run::

    $ make format

so as to beautify your code otherwise travis-ci may fail your unit test.

License
================================================================================

New BSD License


This entry was posted in Christianity, Faith, Family, Life, Photography and tagged beauty, healing, Hope, Ireland, patience, Proverbs, Sligo by lyonsroarforgod. Bookmark the permalink.
What a beautiful visual! The road may be long, Lisa, but with God's help, you can make it ! The bible says patience is a virtue, and it is certainly a virtue that often needs cultivating by keeping our focus on the Lord. Praying for you and believing in you!


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Protecting pets out of harm is important too. These kinds of pets are family and have accumulated to be looked after. With the kids playing and the family dog in the mix, accidents can happen and the last thing we want to do is make a trip to the vet emergency room. This is where a before mentioned child cover or fence is the best preventative measure. Adding a touch of elegance to the room for little cost and getting safety at the same time is known as a wise move. Screens comes in a great variety of styles and looks. Hearth screen is becoming a big decorative statement to your living space. Whether polished brass, bronze, pewter, stainless steel or stained wineglass, a screen bring awareness of a focal point in your room. Fire place screen is a great way to serve a dual purpose, security and elegance. Fire place screen is normally an added layer of safeguards for your largest investment, and a statement of design interior decoration of your home.
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text:
Mutant Ape Planet NFT Developer Arrested For Fraud: Everything You Need To Know
Aurelien Michel, for exampleThe creator of Mutant Ape Planet has been arrested in New York.
Michelle is accused of defrauding investors of up to $2.9 million.
US authorities have arrested the man behind the Mutant Ape Planet NFT project, which is a slam dunk for the popular Mutant Ape Yacht Club. [MAYC] NFT a group. 24-year-old French citizen Aurelien Michel was arrested when he landed at John F. Kennedy Airport in New York.
The creator of the mutant Planet of the Apes has been accused of fraud
according to press release By the US Attorney's Office for the Eastern District of New York on January 5, prosecutors alleged that Michel committed a "fraudulent carpet scheme" and later defrauded investors of $2.9 million.
Prosecutors said:
"As part of the scheme, NFTs were marketed to buyers, who were given false promises of various rewards and benefits designed to increase the demand for and value of newly acquired NFTs. After the NFTs were sold, buyers became 'bored' as none of the promised benefits were offered."
Prosecutors also alleged that cryptocurrencies worth millions were diverted for Michel's personal use. This was proceeds from the sale of Mutant Ape Planet NFTs. Several US agencies, including the Department of Homeland Security and the Internal Revenue Service (IRS), were involved in the arrest.
The creators of the project admitted to organizing carpet pulling
According to prosecutors, Aurelien Michel admitted to formatting the rug on the community's Discord server. He used a false name to make the following confession:
"We had no intention of falling behind, but society has become very toxic."
Homeland Security Agent Evan J. Arvelo stated that the developers had promised multiple deliveries to buyers at the time of the investment. In addition, Internal Revenue Service agent Thomas Fatorusso said Michele defrauded investors by making false claims of betting-themed gifts and tokens and merchandise fundraising.
According to information from CoinStatsMutant Ape Planet had a floor price of 0.01 ETH at the time of publication, and a total volume of 568.59 ETH. Interestingly, the project's trading volume has increased by more than 4,400% in the last 24 hours. Local community members took over the project. They were reportedly trying to bring him back to life at the time of writing.
Posted in BusinessTagged Ape, arrested, Developer, Fraud, Mutant, NFT, Planet
GameFi Rising Star of 2023: Bless Global with P2O Style and 2 Million Pre-Registered Players
A machine learning algorithm indicates the price of Cardano on January 31, 2023

redpajama_set_name: RedPajamaCommonCrawl


Simple abstract class defining basic methods implemented by all objects that need to be stored in a StelSphericalIndex.
Definition at line 28 of file StelRegionObject.hpp.
Implemented in StelObject, and Nebula.


A muffin is somewhat like a small cake, and though it does resemble a cupcake in that they both have cylindrical bases and rounded conical tops, they usually are not as sweet as cupcakes and generally lack frosting; savory varieties (such as cornbread muffins) also exist. They generally fit in the palm of an adult hand, and are intended to be consumed by an individual in a single sitting. A muffin can also mean a different baked good, the smaller, disk-shaped English muffin, although this usage is uncommon outside Britain. As American style muffins are now sold in the UK, the term can refer to either product, the context usually making clear which is meant. There are many varieties and flavors of muffins made with a specific ingredient such as blueberries, chocolate chips, cucumbers, raspberry, cinnamon, pumpkin, date nut, lemon, banana, orange, peach, strawberry, boysenberry, almond, and carrot. These ingredients are then baked into the muffin.
The traditional English muffin is very different from the American variety. The English muffin is yeast leavened and predates the baking powder leavened muffins. This produces a type of muffin with a thick, fluffy pastry and is usually baked as a disk typically about 8 cm in diameter. It is usually split into two, toasted and buttered, and bears a vague resemblance to a crumpet or pikelet. It also is eaten cold with a hot drink at coffee shops and diners. Fannie Farmer (a young adolescent girl) in her Cook Book gave recipes for both types of muffins, distinguishing between "raised" and adding instructions for a version that is nearly identical to today's "English muffin". Here the raised-muffin mixture was cooked in muffin rings on a griddle, and flipped to brown both sides, producing a grilled muffin. Farmer indicated this was a useful method when baking in an oven was not practical.


text:
\section{Introduction}

The recent resurgence of neural networks in reinforcement learning (RL) can be attributed to the widespread success of Deep Reinforcement Learning (deep RL), which uses deep neural networks for function approximation \cite{mnih2015human,mnih2016asynchronous}.
One of the most impressive accomplishments of deep RL is its ability to learn directly from raw images, achieving state-of-the-art results. However, in order to bring the success of deep RL from virtual environments to real-world applications, we must address the lengthy training time that is required to learn a policy.

Deep RL suffers from poor initial performance like classic RL algorithms since it learns \emph{tabula rasa} \cite{sutton1998reinforcement}. In addition, deep RL inherently takes longer to learn because besides learning a policy it also learns a state directly from raw images --- instead of using hand-engineered features, deep RL needs to learn to construct relevant high-level features from raw images.
These problems are consequential in real-world applications with expensive data, such as in robotics, finance, or medicine.

Leveraging humans to provide demonstrations is one method to speed up deep RL. Using human demonstrations in RL is not new \cite{argall2009survey} but only recently has this area gained traction as a possible way of speeding up deep RL \cite{kurin2017atari,vinyals2017starcraft,hester2018learning}.

Speeding up deep reinforcement learning can be achieved by addressing the two problems it is trying to tackle: 1) feature learning and 2) policy learning. In this work, we will focus only on addressing the problem of feature learning by pre-training to learn the underlying features in the hidden layers of the network. We show that by learning better features, an RL agent can achieve better performance without changing its policy learning strategies thus addressing the importance and usefulness of learning good features in an RL problem.
We apply a common technique to deep RL that is widely used to speed up training in deep learning: pre-training a network \cite{erhan2009difficulty,Erhan:2010:WUP:1756006.1756025,yosinski2014transferable}. However, the success of this technique in deep supervised learning is attributed to the large datasets that are available and used to pre-train networks. In deep RL, data are often unavailable or difficult to collect.

In this work, we propose an approach to speed up deep reinforcement learning algorithms using only a relatively small amount of non-expert human demonstrations. This approach starts by pre-training a deep neural network using human demonstrations through supervised learning. Similar work has shown that this step would learn to imitate the human demonstrator \cite{argall2009survey}. However, in this context, pre-training through supervised learning would implicitly learn the underlying features.

We test our approach with two popular deep RL algorithms, the Deep Q-network (DQN) and the Asynchronous Advantage Actor-Critic (A3C), and evaluate its performance in the Atari 2600 games of Pong, Freeway, and Beamrider \cite{bellemare13arcade}. Our results show speed ups in five of the six cases. The improvements in Pong and Freeway were quite large in DQN, and A3C's improvement on Pong was especially large. The generality of this approach means that it can be easily incorporated into multiple deep RL algorithms.

\section{Related Work}
Our work is not precisely transfer learning, but it is similar to one of the existing transfer learning methods in deep learning. In training deep neural networks for image classification, Yosinski et al.~\shortcite{yosinski2014transferable} have shown how transferring the features learned from existing models allow new models to learn faster, particularly when the datasets are similar.
In this work, we use a deep learning classifier as the source network to initialize the RL agent's network.

Existing work on pre-training in RL has shown learning can be improved \cite{abtahi2011deep,anderson2015faster}.
However, their networks have a much smaller number of parameters and state dynamics of the domain are used as network input. In our approach, we use the raw images of the domain as network input and also the RL agent needs to learn the latent features while learning its policy.

Our approach of using supervised learning for pre-training is also similar in spirit to that of Anderson et al.~\shortcite{anderson2015faster}. They pre-train by learning to predict the state dynamics. We instead pre-train by using the game's image frames from the human demo as training data, which are individually labeled by the action taken by the human demonstrator. This setup is similar to how one could derive a policy when learning from demonstration \cite{argall2009survey}.

Another approach to pre-training is to learn the latent features using unsupervised learning through Deep Belief Networks \cite{abtahi2011deep}. Although this pre-training approach differs --- it falls under a different machine learning paradigm --- its goals are similar to our approach in that pre-trained networks learn better than randomly initialized networks.

There are more recent works that leverage humans in deep RL. Christiano et al.~\shortcite{christiano2017deep} use human feedback to learn a reward function. Hester et al.~\shortcite{hester2018learning} similarly pre-train the network with human demonstrations in DQN. However, their pre-training combines the large margin supervised loss and the temporal difference loss, which tries to closely imitate the demonstrator. Our work differs in that only the cross-entropy loss is used
and we focus on the implicitly learned features.

The work of Silver et al.~\shortcite{silver2016mastering} trained human demonstrations in supervised learning and used the supervised learner's network to initialize RL's policy network. They tested this approach in a single domain and a huge amount of expert demonstration data was used to train the supervised learner.
Our work will be the first to provide a comparative analysis as to how this approach impacts deep RL algorithms and how well this approach can complement existing deep RL algorithms when human demonstrations are available. Silver et al.~\shortcite{silver2016mastering} also focus on optimizing the policy learned from humans, while our paper focuses on learning the underlying features. Our work shows that: 1) using only a small set of demonstration data is sufficient enough to gain performance improvements, and 2) a supervised learner can still learn important latent features even when demonstrated human data is from non-experts.

\section{Background: Deep Reinforcement Learning}

An RL problem is typically modeled using a Markov decision process, represented by a 5-tuple $\langle S, A, P, R, \gamma \rangle$. An RL agent explores an unknown environment by taking an action $a \in A$. Each action leads the agent to a state $s \in S$. A reward $r \sim R(s,a, s')$ is given based on the action the agent took and the next state $s'$ it reaches. The goal of an RL agent is to learn to maximize the expected return value $R_t = \sum_{k=0}^{\infty} \gamma^k r_{t+k}$ for each state at time $t$. The discount factor $\gamma \in (0,1]$ determines the relative importance of future and immediate rewards.

\subsection{Deep Q-network}
The first successful deep RL method, deep Q-network (DQN), learns to play 49 Atari games directly from screen pixels by combining Q-learning with a deep convolutional neural network \cite{mnih2015human}. In shallow Q-learning, an agent learns a state-action value function $Q^*(s, a) = \E_{s'}[r+\gamma \max_{a'}Q^* (s', a')|s, a]$, which is the expected discounted reward determined by performing action $a$ in state $s$ and thereafter performing optimally \cite{watkins1992q}. The optimal policy $\pi^*$ can be deduced by following actions that have the maximum Q value, $\pi^* = argmax_{a}Q^*(s,a)$.

Directly computing the Q value is not feasible when the state space is large or continuous (e.g., in Atari games). The DQN algorithm uses a convolutional neural network as a function approximator to estimate the Q function $Q(s, a; \theta) \approx Q^*(s, a)$, where $\theta$ is the network's weight parameters. For each iteration $i$, DQN is trained to minimize the mean-squared error (MSE) between the Q-network and its target $y = r + \gamma max_{a'}Q(s', a';\theta_{i}^-)$, where $\theta_{i}^-$ is the weight parameters for the target network that was generated from previous iterations. All rewards are clipped to 1 when positive, -1 when negative, and 0 when unchanged.
The loss function at iteration $i$ can be expressed as $L_{i}(\theta_i) = \E_{s, a, r, s'} [(y - Q(s, a; \theta_i))^2]$, where $\{s, a, r, s'\}$ are state-action samples drawn from experience replay memory with a minibatch of size 32. The use of a target network, reward clipping, and an experience replay memory are essential to stabilize learning. In addition, the $\epsilon$-greedy policy is used by the agent to obtain sufficient exploration of the state space.

\subsection{Asynchronous Advantage Actor-critic}

There are a few drawbacks of using experience replay memory in the DQN algorithm. First, storing all experiences is space-consuming and could slow down learning. Second, using replay memory limits DQN to off-policy algorithms. The asynchronous advantage actor-critic (A3C) algorithm was proposed to overcome these problems \cite{mnih2016asynchronous}.

A3C combines the actor-critic algorithm with deep RL. It differs from value-based algorithms (e.g., Q-learning) where only a value function is learned --- an actor-critic algorithm is policy-based and maintains both a policy function $\pi(a_t|s_t;\theta)$ and a value function $V(s_t;\theta_v)$ \cite{sutton1998reinforcement}. The policy function is called the actor, which takes actions based on the current policy $\pi$. The value function is called the critic, which serves as a baseline to evaluate the quality of the action by returning the state value $V^\pi(s_t;\theta_t)$ for the current state under policy $\pi$. The policy is directly parameterized and improved via policy-gradient. To reduce the variance in policy gradient, an advantage function is used and calculated as $A(a_t, s_t;\theta,\theta_v) = \sum_{i=0}^{k-1} \gamma^{i}t_{t+i} + \gamma^{k}V(s_{t+k};
\theta_v) - V(s_t;\theta_{v})$ at time step $t$ for action $a_t$ at state $s_t$, where $k$ is upper-bounded by $n$, the number of steps used for n-step return update. The loss function for A3C is $L(\theta) = \nabla_\theta \log \pi(a_t|s_t;\theta)A(a_t, s_t;\theta,\theta_v)$.

In A3C, $k$ actor-learners are running in parallel with their own copies of the environment and the parameters for the policy and value function. This enables exploration of different parts of the environment and therefore observations will not be correlated. This mimics the function of experience replay memory in DQN while being more efficient in space and training time. Each actor-learner performs a parameter update every $t_{max}$ actions, or when a terminal state is reached --- this is similar to using mini-batches, as is done in DQN. Updates are synchronized to a master learner that maintains a central policy and value function, which will be the final policy upon the completion of training.

\section{Pre-Training Networks for Deep RL}

Deep reinforcement learning can be divided into two sub-tasks: feature learning and policy learning. Deep RL in itself has already succeeded in learning both tasks simultaneously. However, learning both tasks also makes learning in deep RL very slow. We believe that by addressing the feature learning task, deep RL agents can better focus on learning the policy. We learn the features by pre-training the network using human demonstrations from non-experts. We assume here that humans provide correct labels through actions demonstrated while playing the game. We will refer to our approach as the \emph{pre-trained model}.

We first apply the pre-trained model approach in DQN and refer to it as the \emph{pre-trained model for DQN (PMfDQN)}. In PMfDQN, we train a multiclass-classification deep neural network with a softmax cross entropy loss function. The loss is minimized using the Adam optimizer \cite{kingma2014adam} with the following hyperparameters: step size $\alpha=0.0001$, stability constant $\epsilon=0.001$, and Tensorflow's default exponential decay rates $\beta$. The network architecture for the classification follows the same structure of the hidden layers of DQN, which has three convolutional layers (\emph{conv1}--\emph{conv3}) and one fully connected layer (\emph{fc1}) \cite{mnih2015human}. The classifier's output layer has a single output for each valid action and is trained using the cross-entropy loss instead of the TD loss. The learned weights and biases from the classification model's hidden layers are used to initialize the DQN network, instead of random initialization. When using all layers of the pre-trained model (including the output layer), normalization of the parameters of the output layer was necessary to achieve a positive result. To normalize the output layer, we keep track of the maximum value of the output layer during training, which is used as a divisor to all the weights and biases during initialization with the pre-trained model. Without normalization, the values of the output layer tend to explode. We also load the human demonstrations in the replay memory, thus removing the need for DQN to take uniform random actions for 50,000 frames to initially populate the replay memory \cite{mnih2015human}.

The pre-trained model method can also be applied in A3C, which we will refer to as the \emph{pre-trained model for A3C (PMfA3C)}. In PMfA3C, we pre-train the multiclass-classifier using the same hyperparameters and optimization method as mentioned in PMfDQN while experimenting with two different network structures. The first network uses a structure with three convolutional layers (\emph{conv1}--\emph{conv3}) and one fully connected layer (\emph{fc1}), but without the long short-term memory (LSTM) cells \cite{sharma2017learning}. The output layer is the same as in PMfDQN. The second network is inspired by one-vs.-all multiclass-classification and multitask learning \cite{caruana1998multitask}. It differs from the first network as it uses multiple heads of output layers where each class or action has its own output layer. Each individual output layer becomes a one-vs.-all classification. During each training iteration, a uniform probability distribution is used to select which output layer to train. In each iteration, gradients are backpropagated to the shared hidden layers of the network. In both multiclass networks, only the hidden layers are used to initialize A3C's network.

Since DQN uses experience replay memory \cite{lin1992self}, it is also possible to pre-train just by loading the human demonstrations in the replay memory. We refer to this experiment as \emph{pre-training in DQN (PiDQN)}. While somewhat naive, this is still an interesting method as it allows the DQN agent to learn both the features and policy without any interaction with the actual Atari environment. However, this pre-training method does not generalize to A3C and/or other deep RL algorithms that do not use a replay memory. We would like to address this in future work by applying this naive approach to an alternative version of A3C that uses a replay memory \cite{wang2016sample}.

Lastly, we conducted additional experiments in DQN that combines PMfDQN and PiDQN, with the goal of exploring whether a combined approach would achieve a greater performance in DQN.

\section{Experimental Design}
\label{experiments}

We test our approach in three Atari games: Pong, Freeway, and Beamrider, as shown in Figure~\ref{fig:atari_games}.
The games have 6, 3, and 9 actions, respectively. We use OpenAI Gym's deterministic version of the Atari 2600 environment with an action repeat of four \cite{openaigym}.

\begin{figure}[H]
    \centering
     \includegraphics[width=0.99\columnwidth]{atari_games}
     \caption{Atari 2600 game screenshot of Pong, Freeway and Beamrider, from left to right.}
     \label{fig:atari_games}
\end{figure}

\begin{table*}[t!]
\caption{Summary of pre-training experiments.}
\label{table:experiments}
\centering
\begin{adjustbox}{width=0.85\textwidth}
\begin{tabular}{l|c}
Method      & Summary  \\ \hline \hline
PiDQN                             & pre-train in DQN for 150,000 iterations, batch size of 32 \\ \hline
PMfDQN                            & initialize DQN with pre-trained model, pre-train for 150,000 iterations, batch size of 32 \\ \hline
PMfDQN+PiDQN                      & initialize DQN with pre-trained model and continue to pre-train in DQN \\ \hline
PMfDQN+PiDQN ($\epsilon=0.1$)     & low initial exploration rate \\ \hline
PMfDQN (random demo)              & pre-train model with random demonstrations  \\ \hline
PMfDQN (no fc2)                   & initialize with pre-trained model excluding output layer \\ \hline
PMfA3C                            & initialize A3C with pre-trained model, pre-train for 150,000 iterations, batch size 32    \\ \hline
PMfA3C (1-vs-all)                 & pre-train model using one-vs-all multi-class classification, longer pre-training \\ \hline
PMfA3C (1-vs-all, 1-demo)         & pre-train model using only one out of the five demonstrated game play \\ \hline \hline
\end{tabular}
\end{adjustbox}
\end{table*}

We use the same network architecture and hyperparameters for DQN as was done in the original work \cite{mnih2015human}. For the LSTM-variant of A3C, we follow the work of Sharma et al.~\shortcite{sharma2017learning} as their work closely replicates the results of the original A3C algorithm \cite{mnih2016asynchronous}. However, note that there are two key differences from the original A3C work. First, while using the same network architecture with three convolutional layers, the fully connected layer was modified to have 256 units (instead of 512) to connect with the 256 LSTM cells that followed. Second, we use $t_{max}=20$ instead of $t_{max}=5$. We use 16 actor-learner threads for all A3C experiments.

In both DQN and A3C, we use the four most recent game frames as input to the network where each frame is pre-processed. We also use the same evaluation technique for both DQN and A3C such that the average reward over 125,000 steps was recorded. In addition, DQN is evaluated using a $\epsilon$-greedy action selection method, where $\epsilon=.05$. In A3C, it is evaluated as a stochastic policy where it uses the output policy as action probabilities.

\subsection{Collection of Human Demonstration}
We are using OpenAI Gym's keyboard interface to allow a human demonstrator to interact with the Atari environment. The demonstrator is provided with game rules and a set of valid actions with their corresponding keyboard keys for each game. The action repeat is set to one to provide smoother transitions of the games during human play, whereas the action repeat is set to four during training \cite{mnih2015human,mnih2016asynchronous}.
During the demonstration, we collect every fourth frame of the game, saving the game state using the game's image, action taken, reward received, and if the game's current state is a terminal state. The format of the stored data follows the structure of the experience replay memory used in DQN.

The non-expert human demonstrator plays five rounds for each game. Each round has a maximum of five minutes of playing time. The demonstration ends when the game reaches the time limit or when game terminates --- whichever comes first.
Table~\ref{table:human_demo} provides a breakdown of human demonstration size for each game and the human performance level.

\section{Results}

This section presents and discusses results from pre-training deep RL's network for DQN and A3C.

\begin{table}[pt!]
\caption{Human demonstration over five plays per game.}
\label{table:human_demo}
\centering
\begin{tabular}{l|c|c|c}
Game      & Worst Score & Best Score & \# of Frames \\ \hline \hline
Beamrider & 2,160       & 3,406      & 11,205  \\ \hline
Freeway   & 28          & 31         & 10,241 \\ \hline
Pong      & -10         & 5          & 11,265 \\ \hline \hline
\end{tabular}
\end{table}

\subsection{DQN}

Using PMfDQN, we trained one multiclass-classification network for each Atari game with the human demonstration dataset. Each training run was conducted using a batch size of 32 for 150,000 training iterations. The number of training iterations was determined to be the shortest number of iterations where the training loss for all games converges approximately to zero. The trained classifiers provided us the pre-trained models which were then used to initialize DQN's network weights and biases.
Figure~\ref{fig:dqn_result} shows the results that PMfDQN speeds up training in all three Atari games. We also tested PiDQN with the same number of pre-training iterations as in PMfDQN. PiDQN for Beamrider has shown some performance improvement with an average total reward of 5,120 compared to DQN's average total reward of 4,894. However, PiDQN in Pong and Freeway either follows a similar learning trajectory as DQN or slightly worse.
Our experiments show although naive pre-training could provide speedups under some cases, supervised pre-training is the essential component when using demonstrations.

To see if we can further improve DQN through pre-training, we used PMfDQN followed by PiDQN with 150,000 pre-training iterations each. Figure~\ref{fig:dqn_result} shows the result for the combined method (PMfDQN+PiDQN). This method showed a slight improvement in playing Freeway but less improvement in Pong and Beamrider when compared to only using PMfDQN. We found these results surprising since we hypothesized that more improvement should be expected with more pre-training.
One possible reason for this could be due to the high initial exploration rate $\epsilon=1$ in DQN at the beginning of training. Under this setting, the agent would be taking entirely random actions until the value of $\epsilon$ has decayed to a much lower exploration rate. In the original DQN, $\epsilon$ is decayed over one million steps, resulting in a replay memory with a good amount of experiences executed through random action \cite{mnih2015human}. This may have an adverse effect on what has already been learned from the pre-training steps. Therefore, we instead initialized $\epsilon=0.1$ when using the PMfDQN+PiDQN combined method.
Results for the combined method as shown in Figure~\ref{fig:dqn_result} revealed that combining PMfDQN with PiDQN using a low initial exploration rate was comparable to PMfDQN by itself and was even better for Freeway. DQN's high initial exploration rate can be detrimental in costly real-world applications. By using a small amount of human demonstration, we can now minimize exploration without affecting agent's overall learning performance.

To measure improvement for each pre-training method, we computed the average total reward for each trial and compared each pre-training method against the DQN baseline. In PMfDQN, the average total reward was higher than the baseline in all three games, although it was only statistically significant for Pong and Freeway.
However, the improvements in both PMfDQN+PiDQN and PMfDQN+PiDQN ($\epsilon=0.1$) were statistically significant in all three games as indicated by t-test with $p<.05$.

\begin{figure}[pt!]
  \centering
    \begin{subfigure}[h]{0.99\columnwidth}
        \includegraphics[width=\columnwidth]{pong_plot_mean_error}
    \end{subfigure}

    \begin{subfigure}[h]{0.99\columnwidth}
        \includegraphics[width=\columnwidth]{freeway_plot_mean_error}
    \end{subfigure}

    \begin{subfigure}[h]{0.99\columnwidth}
        \includegraphics[width=\columnwidth]{beamrider_plot_mean_error}
    \end{subfigure}
    \caption{Performance evaluation of the ablation studies for Pong using DQN. The results are the average testing score over four trials where the shaded regions correspond to the standard deviation.}
    \label{fig:dqn_result}
\end{figure}

\subsubsection{Ablation Studies}

We consider two modifications to PMfDQN to further analyze its performance. In our first ablation study, we replaced human demonstrations with random demonstrations. We were interested in knowing how important it is to use human demonstrations in comparison with using a random agent. We conducted this experiment in Pong and the results in Figure~\ref{fig:dqn_pong_ablation} showed that pre-training with random demonstrations was worse than the DQN baseline. This experiment indicated that there was a need for some level of competency from the demonstrator in order to extract useful features during pre-training. However, even with worse results, our approach appears to still converge to a policy similar to baseline DQN.

In our second ablation study, we excluded the second fully connected layer (fc2) (i.e., the output layer) when initializing the DQN network with the pre-trained model. This will allow us to know if supervised learning does learn important features, particularly in the hidden layers.
Empirically, when excluding the output layer, results in Figure~\ref{fig:dqn_pong_ablation} showed that even though the initial jumpstart was lost, the training time to reach convergence is not different from the time when using all layers. This indicated that it was actually the features in the hidden layers that provided most of the improvement in the training speed. This was not surprising since the output layer of a classifier was trying to learn to predict what action to take given a state without any consideration for maximizing the reward. Additionally, when learning from only a small amount of data where human performance was relatively poor (Table~\ref{table:human_demo}), the classifier's policy would be far from optimal.

\subsection{A3C}
Using PMfA3C, we also pre-trained multiclass-classification networks for each Atari game with human demonstrations, similar to what was done in PMfDQN with a batch of 32 for 150,000 training iterations. Since the network for the LSTM-variant of A3C used LSTM cells with two output layers, we only initialize A3C's network with the pre-trained model's hidden layers. In Figure~\ref{fig:a3c_result}, results showed improvements in the training time in both Pong and Beamrider, with a much higher improvement in Pong.

\begin{figure}[pt!]
    \includegraphics[width=0.99\columnwidth]{pong_plot_mean_error_ablation}
    \caption{Performance evaluation on the ablation studies for Pong using DQN. The results are the average testing score over four trials where the shaded regions correspond to the standard deviation.}
    \label{fig:dqn_pong_ablation}
\end{figure}

\begin{figure}[pt!]
  \centering
    \begin{subfigure}[h]{0.99\columnwidth}
        \includegraphics[width=\columnwidth]{pong_a3c_lstm}
    \end{subfigure}

    \begin{subfigure}[h]{0.99\columnwidth}
        \includegraphics[width=\columnwidth]{freeway_a3c_lstm}
    \end{subfigure}

    \begin{subfigure}[h]{0.99\columnwidth}
        \includegraphics[width=\columnwidth]{beamrider_a3c_lstm_full}
    \end{subfigure}

    \caption{Performance of baseline and pre-training using A3C. The x-axis is the number of training steps which is also the number of visited game frames among all parallel workers with frame skip. The y-axis is the average testing score over four trials where the shaded regions correspond to the standard deviation. Note that all PMfA3C experiments do not use the output layer from the pre-trained model.}
    \label{fig:a3c_result}
\end{figure}

However, there was no improvement in Freeway. This was within our expectation since the baseline performance of Freeway was poor in the original A3C work \cite{mnih2016asynchronous} (shown in Figure~\ref{fig:a3c_result} baseline). We would like to emphasize that our approach focused on learning features without addressing improvements in policy --- no improvements in Freeway with our approach were expected. Freeway in A3C needs a better way of exploring states in order to learn a near-optimal policy for the game. This is something we will try to address in future work.

With strong improvements observed in A3C, can we still gain further improvements if we pre-train our classification network longer? We then tried longer training using the one-vs.-all multiclass-classification network with shared hidden layers. Since each class or action was trained independently, we can now observe the different convergence of the training loss for each class. This allowed us to use the same technique of training until the training loss for all classes was approximately zero. Using the one-vs.-all classification, we pre-trained for 450,000 iterations in Pong and 650,000 iterations in Beamrider. Training longer resulted in a very large improvement in pong and a slight improvement for Beamrider, as shown in Figure~\ref{fig:a3c_result}.

\begin{figure*}[t!]
  \centering
    \includegraphics[width=0.99\textwidth]{weights_conv1}
    \caption{Visualization of the normalized weights on Pong's first convolutional layer using PMfA3C. The weights (filters) are from a pre-trained classification network trained for 150,000 iterations (left image), and from the final weights after 50 million training steps in A3C (right image). To better illustrate the similarity of the weights, we provided two zoomed-in images of a particular filter from pre-trained conv1 (green box) and final conv1 (blue box).}
    \label{fig:weights_visual}
\end{figure*}

The last experiment we conducted was to test whether important features could still be learned even with a much smaller number of demonstrations, in this case, a single round of the game that was only five minutes of demonstration. We used one-vs.-all classification network to pre-train for Pong with only 2,253 game frames with 250,000 training iterations and similarly for Beamrider with 2,232 game frames with 300,000 training iterations. In Figure~\ref{fig:a3c_result}, results for both Pong and Beamrider show improvement with only this small number of demonstrations.
It was even more remarkable in Beamrider, as results were as good as pre-training with the full set of the human demonstrations.

\subsection{Additional Analysis}
In order to understand what was accomplished with pre-training, we looked closer at the weights of the network layers (i.e., filters) to determine how much pre-trained features contributed to the final features learned. Thus, we further investigated how similar the initial weights $\hat{\theta}$ of a deep RL network were to its final weights $\theta$ for each layer after learning a near-optimal policy. We can quantify the similarity by finding the difference between the weights using the mean squared error $\text{MSE}=\frac{1}{n} \sum_{i=1}^{n} (\hat{\theta}_i - \theta_i)^2 $. Smaller MSE indicates a higher similarity between layers. Table~\ref{table:mse_similarity} shows that there was a higher similarity in the pre-training approach compared to random weight initialization. Furthermore, we looked at the visualization of each hidden layer and observed that the weights learned from classification and used as initial values in deep RL's network provided features that were retained even after training in deep RL as shown in Figure~\ref{fig:weights_visual}.

\begin{table}[t]
\caption{Evaluation of the similarity of features for each hidden layer. The mean squared error (MSE) is computed between the weights from a randomly initialized A3C network (baseline) and the final weights. Similarly, when using a pre-trained model as the initial weights.}
\label{table:mse_similarity}
\centering
\begin{adjustbox}{width=\columnwidth}
\begin{tabular}{l|c|c|c|c}
\multirow{2}{*}{Layer} & \multicolumn{2}{c|}{MSE (Pong)} & \multicolumn{2}{c}{MSE (Beamrider)} \\ \hhline{~----}
       & Baseline                & Pre-train              & Baseline                & Pre-train \\ \hline \hline
conv1  & $1.03\times10^{-2}$ & $3.94\times10^{-3}$ & $3.32\times10^{-2}$ & $2.53\times10^{-2}$ \\ \hline
conv2  & $8.02\times10^{-3}$ & $8.00\times10^{-4}$ & $8.50\times10^{-3}$ & $4.35\times10^{-3}$ \\ \hline
conv3  & $7.13\times10^{-3}$ & $3.26\times10^{-4}$ & $7.11\times10^{-3}$ & $2.39\times10^{-3}$ \\ \hline
fc1    & $9.57\times10^{-4}$ & $7.54\times10^{-5}$ & $1.07\times10^{-3}$ & $3.29\times10^{-4}$ \\ \hline \hline
\end{tabular}
\end{adjustbox}
\end{table}

\section{Discussion and Conclusion}
The pre-training approach worked very well in Pong. This success can be explained by the human demonstration data the classifier was pre-trained with, and the simplicity of Pong environment. Pong's states are highly repetitive when compared to the other game environments that are more dynamic. The Beamrider has the most complex environments among all three games because it has different levels of varying difficulty. Although Freeway's game state is also repetitive, A3C's inability to learn a good policy is a problem that leans more towards policy learning, which is not addressed in our approach.

Human demonstrations are an essential part of the success of our approach. It is important to understand how the demonstrator's performance and the amount of demonstration data affect the benefits of pre-training in future work. Future work will consider using recently released human demonstration datasets for Atari \cite{kurin2017atari} and Starcraft II \cite{vinyals2017starcraft}.

Another issue that needs to be addressed in regards to the human demonstrations is that they suffer from highly imbalanced classes (actions). This is attributed to: 1) sparsity of some actions (e.g., the torpedo action in Beamrider is limited to three uses at each level), 2) actions that are closely related (e.g., in Beamrider, there is a left and right action plus combined actions of left-fire and right-fire --- a demonstrator would usually just use the native actions of left and right action alone and use the fire action by itself), and 3) games having a default no-operation action.

In a previous study, the authors show that the classifier will learn a policy that tends to bias towards the majority classes when the imbalance problem is not addressed \cite{he2009learning}. It is interesting that the classifier is still able to learn important features without handling this issue. We see this as an interesting future work and hope to explore if better features can be learned by handling the class imbalance problem, therefore, leads to further improvements.

As we investigate further ways to improve our approach, we know there is a limit to how much improvement pre-training can provide without addressing policy learning. In our approach, we have already trained a model with a policy that tries to imitate the human demonstrator thus we can extend this work by using the pre-trained model's policy to provide advice to the agent (e.g., \cite{2017IJCAI-Wang}).

Overall, learning a policy directly from raw images through deep neural networks is a major factor why learning is slow in deep RL. This paper has demonstrated that our method of initializing deep RL's network with a pre-trained model can significantly speed up learning in deep RL.

\begin{acks}

The A3C implementation was a modification of \url{https://github.com/miyosuda/async_deep_reinforce}. The authors would like to thank Sahil Sharma and Kory Matthewson for providing very useful insights on the actor-critic method. We also thank NVidia for donating a graphics card uses in these experiments.

\end{acks}

redpajama_set_name: RedPajamaArXiv


Our Club's effort to support the Association for the Prevention and Handling of Violence in the Family was met with much enthusiasm by all our Rotaractors, our friends and the general public.
Through our mini marathon of December Christmas Bazaars and the sale of our 2012 Rotaract Calendar not only did we manage to raise awareness about the Association's programmes and initiatives but we also managed to raise over 2000 euros as net proceeds in order to purchase some much needed household goods for the Shelter.
Fortunately after having bought all items on the Association's "wish list" there was a remaining sum of 600 euros.
On 28th January 2012 a team of our Rotaractors went over to the Associations offices to drop off our 'gifts', the remainder of the 2012 Calendars (to be sold or given at the Association's discretion) and of course the amount of 600 euros all of which were handed over to the Association's Programme Officer, Mr Marios Nikolaou.
He thanked our Club warmly for showing such keen interest in the Association's work and for our Club's efforts to support them and wished us all the very best in our next endeavours.
Our Rotaractors reciprocated the wishes and assured Mr Nikolaou that our Club will stand close to the Association for any further assistance in the future.
We kindly ask you to spare a few minutes of your time by clicking on the link below to find out more about the Association and their efforts.


text: 1 September 2022Salt Lake City, Utah Police on an investigation at 900 South 200 West. No SLCPD officers were involved with this incident. Salt Lake City, Utah Police on scene of what is being described as an officer-involved shooting near 900 S 200 W. UTAH Police were involved here. Clearfield, Utah Clearfield Police arrested a suspect after a shooting at the city cemetery on Wednesday night. They responded to 911 calls of shots fired around 9:30 PM near 1200 South State StreetClearfield, Utah Police investigating a shooting where someone was transported to the hospital with a gunshot wound Wednesday nightSalt Lake City, Utah Provo police are investigating a violent crash that killed a brother and sister who were on their way to schoolProvo, Utah A young brother and sister are dead after a car swerved off the road and struck them. The driver also died in the crash, police sayMillcreek, Utah Firefighters have responded to a house fire near the Layton Hills Mall; Hill Field Road is closed in the areaSalt Lake City, Utah The Ogden City Fire Department is asking for assistance from anyone who may have information on a fire that occurred on 34th St. and Washington BlvdSalt Lake City, Utah The Ogden City Fire Department is asking the public for information about a fire that occurred on the 3400 block of Washington Boulevard over the weekend. Details here:Salt Lake City, Utah Salt Lake police warn to avoid area near Chadwick Street and Parkway AvenueSalt Lake City, Utah Officers are on scene with a barricaded individual near Chadwick Street and Parkway Avenue. Please avoid the area if possible. A PIO is on scene at Parkway Avenue and Dearborn StreetSalt Lake City, Utah Officers are on the scene of an aggravated assault at Pioneer Park in Salt Lake CitySalt Lake City, Utah Police are investigating an aggravated assault that occurred at Pioneer Park Friday eveningSalt Lake City, Utah Officers are investigating an aggravated assault at Pioneer Park. One person has serious injuries. One suspect is in custody. The park is now closed and secured. Additional details will be released once confirmed. Media staging is at 336 West 300 SouthSalt Lake City, Utah Officers are investigating an aggravated assault at Pioneer Park. One person has minor injuries. One suspect is in custody. The park is now closed and secured. Additional details will be released once confirmed. Media staging is at 336 West 300 SouthDraper, Utah A suspect has been arrested in connection to a fatal shooting at a Draper apartment complexSalt Lake City, Utah Police say that at one point, "the vehicle drifted to the right on a two way road and hit an unoccupied vehicle at 4476 South 1175 WestTaylorsville, Utah Police are investigating a shooting scene in Taylorsville. Two people were found dead in the area of 3600 w 4700 s. A male and female in their 30's. One person driving by said their car was hit by a bullet. They aren't injured. Taylorsville, Utah @TVPDUtah is investigating a double-fatal shooting near 3600 West 4700 South Taylorsville, Utah 2 people are dead in Taylorsville. Police are telling us it's a man and a woman in their 30's. Police originally responded to reports of shots fired at 3600W 4700S., Utah Police say to avoid 3300 South from State Street to Main Street in South Salt Lake because water main repairs, Utah South Salt Lake police are asking drivers to avoid 3300 South between State Street and Main Street after a sink hole opened up in the road. No word yet on how severe the damage isSalt Lake City, Utah This afternoon, officers went to Liberty Park on reports of a man threatening to shoot people while holding a gun. Officers arrived & found the man and the weapon, a BB gun. The suspect, 33, will be booked into jail. There were no injuries or BBs firedDraper, Utah Draper Police are investigating an early morning shooting at The Heritage apartment complex. One man was killed and police are still looking for a suspect. Police don't believe there's any danger to the publicDraper, Utah Draper PD investigating a homicide @ The Heritage Park Apts, off of State St. and 11800 South, just after 1:00 AM they responded to a "shots fired" call & arrived to find a man dead from at least one gunshot wound, no info yet on victim or suspect, more on Fox 13's Live at 11:00Salt Lake City, Utah A Centerville woman tells ABC4 that a man who tried to run her over as she unloaded groceries from her car on Tuesday started a fire on a hill seconds after running into two electrical boxesMurray, Utah Salt Lake Police seeking info on pickup driver they say hit building, caused gas leak, fled on bike #Utah #SaltLakeCity @slcpd @slcfire #hitandrun #gasleak via @GephardtDailySalt Lake City, Utah We and @slcfire are investigating a hit and run at 2050 South Redwood Road. A truck hit a building, causing a gas leak. Southbound traffic on Redwood is detoured at 1900 South. Please avoid the areaSalt Lake City, Utah As people spill out of nightclubs in the early weekend hours, assaults and brawls have unfolded on Pierpont Avenue this summer. Police and businesses are working together to prevent violenceSalt Lake City, Utah A 21-year-old Provo man swerved into oncoming traffic on Old Highway 40 in Summit County and collided with a GMC truck, police say


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By Harsh
[ Software Engineer and Developement | Hike Messenger ]
(for Free) Watch Harsh's full Career Talk on Software Engineering & Development
(for ₹ 100) Do a Self Assessment on Software Engineering & Development to calculate your Dream Index, which is defined as:
According to Harsh your chances of success in Software Engineering & Development is __%
What is Software Engineering & Development?
Software Engineering & Development is a great Career option. One should first understand What a Career in Software Engineering & Development entails before investing time and effort to figure out How to start a Career in Software Engineering & Development. Just like you would normally not trust a non Doctor with names of medicines, you should also not trust opinions about Software Engineering & Development from non professionals.
Harsh is an experienced professional with 3 years & 1 month in Software Engineering & Development. Harsh defines Software Engineering & Development as:
How Harsh got into Software Engineering & Development?
After completing B Tech (Hons) in Computer Science & Engineering from Dehradun Institute of Technology, I started working as a Software Engineer at Hike Messenger. My role as a software engineer was to explore and dive into various aspects of big-data technologies and machine learning, fixing regular issues/bug, developing content visualization console (WT-Forms, Python, Flask, HTML, CSS, JS) etc. I have also worked as a Software Developer Research Intern at Indian Institute of Remote Sensing (IIRS), Indian Space Research Organization (ISRO).
Harsh's Talk on Software Engineering & Development
1) Software Engineering & Development
Next, it covers Education. In this Talk Education was discussed as theoretical topics needed to excel at Software Engineering & Development, and not as Qualifications needed to enter Software Engineering & Development. Here is a list of Education items required for Software Engineering & Development:
2) Data Structures & Algorithm
3) Programming Languages
4) OOPS Concepts
5) Latest Technology
6) S/W Development Design Pattern
Then, the Talk focuses on the most important component that is Skills. Anyone aspiring for Software Engineering & Development needs to understand that Skill development is crucial for success. It discusses following headings for Skills in Software Engineering & Development:
7) Analytical & Logic Design
8) Soft Skills
9) SDLC
10) Patience, Persistence, Perseverance
11) Project Management
12) Empathy
13) Time Management
14) Team Work
Positives of Software Engineering & Development are quite interesting. These are the Positives of Software Engineering & Development:
15) Continuous Learning
16) Vast Career Opportunities
17) Good Salary
19) Global Exposure & Networking
Challenges define Software Engineering & Development as much as the Positives of the same:
20) Up to Date with Technology
21) Desk Job
22) Different S/W Versions
23) Meeting the Requirements
24) Collaborative Working
25) Availability & Responsibility
In the final section of the Talk, Harsh talks about How a day goes in a Career in:
26) Software Engineering & Development
Harsh's LifePage:
https://www.lifepage.in/page/harsh
LifePage Career Talk on Software Engineering & Development
https://www.lifepage.in/careers/software-engineering-and-development
https://www.youtube.com/watch?v=yiKTimDFMZ0
(Software Engineering & Development, Harsh Verma, Hike Messenger, Software Engineering, Software Development Process, Development Life Cycle, Product Management, Information Technology, IT, CS , Data Science)
LifePage has created the largest knowledge repository of Careers in the whole world. I salute the efforts of Team LifePage. I also encourage you to learn more about LifePage on: https://www.lifepage.in
"I did Schooling from The Doon School, Dehradun after which I pursued an Undergraduate Degree in Information Systems from Brock University in Canada. I did a Masters degree in Information System Management from Carnegie Mellon University in Pittsburgh, USA."
Technology Entrepreneurship
Dr Anil Chauhan
ENT Consultant | VelMed Hospital
Group Manager, North India | Titan Company Ltd.
Dr Sanjay Sujata
Gynaecologist | Sanjay Maternity Centre
Ravi Mathur
Special Secretary | Government of India
Career in Acting:
https://pasteid.fledix.com/vquy
Career in Hotel Management:
https://www.careerplan.space/2020/01/what-is-career-in-hotel-management.html
Career in Social Entrepreneurship:
https://www.jobdiscussion.online/2020/01/how-much-can-i-make-in-career-in-social.html
Career in Flair Bartending:
https://www.careerresource.website/2020/01/is-flair-bartending-good-career-option.html
Career in Homeopathy:
https://www.prosebox.net/entry/982922/what-is-a-career-in-homeopathy/


These proclamations come after the Wyoming State Legislature signed a joint resolution in February recognizing NOLS' 50-year commitment to education and responsible land stewardship in Wyoming.
Rep. Lummis stated her pride in the Cowboy State being the hometown of the leader in wilderness education.
"NOLS is headquartered in Wyoming, in Lander, and we are proud that NOLS's mother ship is in our dear State," she said.
Sen. Barrasso's statement noted his support for NOLS as a doctor, a legislator and a leader. He stressed the importance of wilderness medicine training, the impact of the NOLS Environmental Stewardship and Sustainability department's work and the lifelong lessons of "hard work, sacrifice and appreciation and respect for nature" taught on NOLS courses.
"I am confident in the future leadership of our communities and Nation because I know tomorrow's leaders are receiving NOLS instruction and experience today," he declared.
Both legislators concluded their addresses by congratulating NOLS on its 50th anniversary.


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Parsons, Rev. Jackson
Rev. Jackson Harold Parsons, Sr., 95, was born in Cherry Creek, NY on August 23, 1922 and went to be with his Lord and Savior on Wednesday May 23, 2018. He was the son of the late Harold B. Parsons and Edith Abbey Parsons and the second oldest of 14 children.
Jackson graduated from Cherry Creek High School in 1940, and after training in welding, worked for the American Locomotive Company in Dunkirk, NY through the fall of 1942. Then with World War II in progress, he enlisted in the Army Air Corps on October 14, 1942. After training, he served as a propeller specialist on B26 and B25 bomber aircraft that were being prepared for pilot training in Kansas and Oklahoma.
While on furlough, Jackson married his sweetheart, Iola A. Chapman, on October 22, 1943 in their home church in Cherry Creek, NY. After discharge from the Air Force, he felt called to the ministry in the Methodist Church. He graduated from Houghton College in 1951 with an A.B. Degree and in 1956 with a Master of Divinity degree from Christian Theological Seminary.
While in seminary he served in churches in Indiana and Illinois, and was ordained a Deacon, courtesy of the Methodist Illinois Annual Conference. Bishop Wicke ordained him as Elder in the Western PA Annual Conference in June 1956.
He served churches in the Western PA Conference of the United Methodist Church in Sheffield and Barnes, Epworth Church in New Castle, Trinity Church in Erie, Grace Church in Oil City, and Evangelical Church in Corry. He retired in 1986 and then served Lake Pleasant, Union City and Emmanuel Church, North East until 2000.
Rev. Parsons served on various district committees and enjoyed playing golf and bowled until well into his 80's.
In addition to his parents he was preceded in death by his loving wife of 64 years, Iola Arlene Chapman Parsons who died on December 7, 2007; seven brothers, James, Glenn, Harold, Howard, Floyd, Jerry, and John Parsons; and four sisters, Mary Johnson, Edith Seekins, Donna Clark, and Ardith Schuckers.
Survivors include his four children, Jackson H. Parsons, Jr. (Kathy) of Cambridge Springs, Karen G. Duncan (John) of N. Chesterfield, VA, Mark Parsons (Sherrie) of North East, and Colleen K. Barnett (Darrell) of North East; 10 grandchildren; 22 great grandchildren; two sisters, Eva Baker and Pauline Bell; and many nieces and nephews.
Friends may call at the Russell C. Schmidt & Son Funeral Home Inc., 5000 Wattsburg Rd., on Wednesday from 4 to 7 p.m. and on Thursday morning at Wattsburg United Methodist Church, 9904 Jamestown Rd., Wattsburg, 16442 from 9 a.m. until the time of services there at 10:00 a.m.. Burial will follow in Wattsburg Cemetery


While searching the skies for black holes using the Spitzer Space Telescope Deep Wide Field Survey, Ohio State University astronomers discovered a giant supernova that was smothered in its own dust. In this artist's rendering, an outer shell of gas and dust — which erupted from the star hundreds of years ago — obscures the supernova within. This event in a distant galaxy hints at one possible future for the brightest star system in our own Milky Way.
A giant star in a faraway galaxy hasended its life with adust-shrouded whimper instead of the more typical bang, scientists say.
Astronomers have never-beforeobserved such an event, butthink they were likely more commonin the universe'syouth. And a similar dust squelching may await the brightest starsystem in ourgalaxy if it explodes, scientists said.
A supernova is the explosive end to ahuge star around eightor more times the mass of our sun. One occurs when a massive star hasexhaustedits supply of fuel and can no longer sustain itself. Stars greater than20times the mass of the sun can collapse in on themselves to formstellar black holes.
It was black holes, of a more massivevariety, thatresearchers were searching for when they discovered the fizzledsupernova whileporing over data from NASA's Spitzer Space Telescope.
The astronomers were searching for supermassiveblack holes at the centers of galaxies by identifying cosmichot spots ?the heat signature radiated by the disks of material around such blackholegiants.
One very hot spot, which appeared inAugust 2007, didn'tmatch the signature of a black hole. It also burned hot, and then fadedawayquickly, disappearing entirely in March 2008.
"Over six months, it released moreenergy than our suncould produce in its entire lifetime," explained study team memberSzymonKozlowski, now of Warsaw University Observatory in Poland.
The fast flare-out, which occurred ina galaxy about 3billion light-years from Earth, suggested the team had found a supernova.But these massive explosions typically release most of their energy aslight,not heat.
The temperature of the mystery objectwas around 1,292degrees Fahrenheit (700 degrees Celsius). The research team wonderedwhat couldabsorb huge amounts of light energy and dissipate it as heat.
Their answer: dust, and a lot of it.
The scientists, led by astronomerresearcher ChristopherKochanek of Ohio State University, worked backward to determine whatkind ofstar could have spawned the supernova, and how the dust was able tomuffle theexplosion.
They found that the star was mostlikely a giant, at least50 times more massive than our sun. Such massive stars typically belchcloudsof material as they near the end of their existence.
At least two such ejection eventswere required to createthe dust required for the events Kochanek and his team spotted. Thefirstoccurred about 300 years before the supernova exploded, and the othercame justfour years before the explosion. The dust and gas from both ejectionsthen remainedaround the star in the form of two slowly expanding shells.
The younger inner shell ? createdfrom the second dustejection event ? would be very close to the star, while the outer shellwouldbe much farther away.
"We think the outer shell must benearly opaque, so itabsorbed any light energy that made it through the inner shell andconverted itto heat," Kochanek said.
Stars probably choked on their owndust much more often inthe distant past, according to the researchers.
"These events are much more likely tohappen in asmall, low metallicity galaxy," said study team member KrzysztofStanek,also of Ohio State. "Low metallicity" refers to a young galaxy thathasn?t been around long enough for its stars to fuse hydrogen andhelium intoheavier, more complex elements astronomers call metals.
The research is detailed in a recentonline issue of theAstrophysical Journal.
Astronomers will likely find moresuch supernovae in thenear future, the researchers said, especially with the help ofinstruments suchas the Wide-Field Infrared Survey Explorer, an infrared space telescopelaunchedby NASA in December 2009.
"I would expect WISE to see 100 ofthese events in twoyears, now that we know what to look for," Kozlowski said.
Because of the unfavorable alignmentof the star's host galaxywith Earth and our sun, astronomers were not able to see what thedust-shroudedsupernova event looked like to the naked eye as it was happening.
But the researchers said the star mayappear to brighten adecade or so from now, when the shockwave from its explosion reachesthe innerdust shell and sends it crashing into the outer shell.
A similar light show could also occurcloser to home,according to the scientists. EtaCarinae, the brightest star system in our Milky Way, wouldprobably bedust-smothered if it exploded, they said.
?If Eta Carinae went supernova rightnow, this is what itwould probably look like,? Kochanek said.
The two stars known to make up EtaCarinae are 7,500 light-yearsaway, and they host a distinctive dust shell called the HomunculusNebula, among other layers of dust. Astronomers believe thatthe nebula wascreated when the larger of the two stars underwent a massive eruptionaround 1840,and that future eruptions are likely.


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More language
11 Best Movies to Watch In English
Fruitvale Station, 2013
The true story of Oscar Grant III, a 22-year-old black man on the last day of 2008, where his will to change is challenged by his past and the police. You've probably read and heard a lot about young black men's' encounters with the police, and for this reason, you might feel like skipping this film. Don't. Produced by Academy Award winner Forest Whitaker, it is compassionate and powerfully told, that it surpasses the sadness of its subject matter to be a celebration of life. It is an extraordinary film and important watch.
Her, 2013
Her is a movie with plenty of heart, in all its variations: love, sadness, hope and confusion. Through perfectly crafted production, the futuristic surroundings where the movie takes place will feel familiar only few minutes after it begins. This added to the amazing performance by Joaquin Phoenix delivers an unprecedented study of moving on from relationships that once seemed to last forever on one hand, and of the pursuit of intellectual love on the other. Her is thought-provoking in the most beautiful way possible, it narrates a relatable human being's story. It is one of those movies you will keep remembering every once and a while in different contexts of your life.
High Fidelity , 2000
This might just be the most insightful movie about men. Watch if you are a guy and you will cringe endlessly from seeing yourself in the characters, and if you are a girl you should also watch it to laugh and understand the men around you better (yes, it is that insightful). Rob Gordon, a music fanatic who owns a record store, tells the stories of how his relationships ended, included the one ongoing. So if you are asking if this is a romantic comedy about a man trying to move on from a breakup, yes, it is. And it Works. High Fidelity is in fact funny, interesting and comes with a unique look at relationships. But it is mostly simple and entertaining, and with perfect performances from John Cusack and Jack Black as well as an immaculate soundtrack, it is a must-watch.
Enough Said, 2013
Enough Said is a realistically sweet movie that handles serious emotions without compromising its comedic chops. It portrays the difficulties and doubts that come with post-divorce relations in a somewhat goofy and original way. James Gandolfini is impeccably wonderful in his last lead role, and joined by Julia Louis-Dreyfus they generate exquisite chemistry right from their first scene together all the way until the movie ends. Like many other beautiful movies such as Your Sister's Sister, Enough Said is hilarious, romantic, but to that it adds being smart and realistic. You'll love it.
Your Sister's Sister, 2012
The acting... oh the acting! Your Sister's Sister is a fantastic comedy which makes great use of the amazing talents and suitability of its cast, including the criminally underused Emily Blunt. Far smarter, quicker and grown-up than most other Rom-Coms, it's a film built on secrets, lies and, yes, love, sex and family.
Columbus, 2017
What goes well with a love story? Creative architecture. Columbus the movie is such a great and genuine exploration of this idea, filmed in Columbus the city - a weird experimental hub for architecture that actually exists in real life! After his architect father goes into a coma, Jin (John Cho), a Korean-born man finds himself stuck in Columbus without a foreseeable end. In Korean tradition when a parent dies, the son should be in the same place physically otherwise they can't mourn. While waiting to see what will happen to his father he meets Casey (Haley Lu Richardson), an aspiring architect herself, who's also stuck in Columbus because of her mother. This is a beautiful movie with real-life issues and situations, to be especially appreciated by viewers who don't mind a slow narrative in exchange for a meticulously-crafted movie.
Up in the Air , 2009
Ryan Bingham (George Clooney) is a corporate axman, he comes in a fires people when the managers are too afraid to do it themselves. The nature of his work requires a lot of flying, short lived meetings in transit zones and he absolutely loves it, and he has a certain goal in mind. When the company tries a new approach to corporate downsizing he has to change his way and view of life. It's full of cynicism and warmth. If you are familiar with Jason Reitman's previous work, you'll feel right at home, if you don't : Get to it!
Frances Ha, 2013
Frances (Greta Gerwig) lives in New York – but not the glamorous NYC of Woody Allen movies. Taking place primarily in the gritty and rapidly gentrifying North Brooklyn, the black and white film paints a picture of an extended adolescence. Focusing on the goofy and carefree Frances, who loses her boyfriend, her best friend and her dream of being a dancer. She moves in with two guys, both of whom are more successful than her, and becomes even more determined to fulfil her goals, impractical as they may be. Fans of HBO's Girls and other odes to not being a "real person" yet will love this film.
Obvious Child, 2014
This is a revelation of a movie for its simplicity in handling a pretty serious and dark subject. It's the story of a generally immature and newly unemployed stand-up comic in New York and her unplanned pregnancy with a man that was supposed to be a fling, and it's surprisingly funny and yet rather touching. I can't think of many actresses who would've fit the bill quite like Jenny Slate. Not only is she hilarious, but her treatment of a generally sensitive issue from the honest, crass point of view of a down-on-life, New York-er leaves you drowning in empathy for her. I recommend this for anyone looking to cuddle up, have a few little clever laughs and feel all tingly in the chest-al area.
Also see: Best Films
Best Films are our staff picks, they're all movies rated 8.0 and above. Here, we selected three for you.
St. Vincent, 2014
In this comedy/drama, Bill Murray plays an aged, dispirited war veteran named Vincent who openly disdains most people and gives little attention to anything beyond alcohol and horse racing. Living a life of solitude in Brooklyn, everything takes a turn when a young single mother (Melissa McCarthy) and her son Oliver move in next door. Vincent eventually takes on the responsibility of watching over Oliver when Maggie is at work. Murray is perfectly unpleasant in his darkly comedic role, as his relationship with Oliver evolves despite his own misgivings, providing young Oliver (Jaeden Lieberher) with the fatherly/grandfatherly presence he desperately needs. Though somewhat formulaic, St. Vincent rises above expectations by way of great dialogue, favourable performances from all of the leads, and an unbelievably touching finale that will melt your heart. Much better than you probably expect—definitely check this one out.
Serenity, 2005
Serenity is a futuristic sci-fi film that serves as a feature-length continuation of the story-line from the TV program Firefly (2002-2003). The story revolves around the captain (Nathan Fillion) and crew of the titular space vessel that operate as space outlaws, running cargo and smuggling missions throughout the galaxy. They take on a mysterious young psychic girl and her brother, the girl carrying secrets detrimental to the intergalactic government, and soon find themselves being hunted by a nefarious assassin (Chiwetel Ejiofor). The first feature-length film from Joss Whedon (The Avengers), Serenity is a lively and enjoyable adventure, replete with large-scale action sequences, strong characterizations and just the right touch of wry humor. An enjoyable viewing experience that stands alone without demanding that you have familiarity with the original program beforehand.
I, Daniel Blake, 2016
Revealing the gaps in the social safety net, I, Daniel Blake, is a tale centered around a blue collar worker navigating the welfare system in England. At a time where class and social mobility could not be more politically salient, this film calls into question the notion of the "citizen" and exposes the inaccessibility to the social protections in which one presumes entitlement.At the forefront of this, is a heart-warming parable of paternal companionship between Daniel (played by Dave Johns) and a single mother – Katie – (played by Hayley Squires) who is wading through similar terrain. The acting in the film is unfathomably raw which cultivates the deepest source of gut wrenching compassion. Ken Loach has created a film that exposes the true power of empathy, leaving you feeling helplessly human.
That's all for the 11 Best Movies to Watch In English. Move to the latest movies Move to the latest shows


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Maggie Every Day
Writing, Recipes, Theater, Travel, Discussion, etc.
TV – Jessica Jones (Netflix)
© 2015 – Netflix
I love comic books about lady superheroes. Lately I've been digging Captain Marvel and Ms Marvel, but when Netflix announced their lineup of Marvel shows with Jessica Jones I didn't know who she was. I also didn't read any of the comics ahead of time because I wanted to go in with a fresh brain and the opportunity to enjoy this show for what it was – without prejudice about "why didn't they do it this way?"
So – remember back when I watched Daredevil? I don't remember if I said this in my review, but it was a fairly dark show – on the level of the recent Batman movies where it's not sugar-coating things, and it's much grittier, less cartoon-y. Well, compared to Jessica Jones, Daredevil is sunshine and cupcakes, because JJ is f'd up. But let me explain.
Jessica Jones is a private investigator, and it turns out she also has powers. At one point she used them as a superhero, but retired from that game, and decided to use her skills in her business. She has a tragic backstory where she was in a car accident with her family, and she was the only one who survived. She was adopted by a stage mother, looking for a good story for her child-actress daughter to use for good publicity. Jessica and the daughter (Patsy/Trish) became lifelong friends. But at some point, Jessica was found by Kilgrave – a villain with the power to make people do whatever he wants just by telling them. And he makes Jessica his girlfriend…against her will. The story of this series is after Jessica has escaped Kilgrave's influence, and how he comes back into her life, and terrorizes New York City.
I'm not going to share a lot of spoilers because that would require recapping almost the entire season, and I don't have the ability to sum it up quickly, but if you want zero spoilers…go straight to the trailer at the bottom of the page for spoiler-free final thoughts.
And Kilgrave is the part that makes this show so dark and twisted. Perhaps because so many of us have this fear of being out of control – of not being able to do the things that we want – that it makes him so scary. He could make your sole purpose in life to find a slow and torturous way to kill yourself, and you would have no choice. He could tell you to kill others, and again – no choice. There are a lot of other articles out there that talk about how Kilgrave is the best villain because of this and other factors. He is so scary himself, and yet, he's never the one doing the evil things – he just suggests them, and then others do these things for them. It doesn't help that David Tennant who plays Kilgrave is so handsome and charming, and so many of us know him from his time on Doctor Who. Here, he his very nearly the devil incarnate, and he plays the role so slickly – with such manic charm that it's easy to see why he was the choice for the villain who can make others do his bidding.
And beyond Kilgrave scaring the pants off us all, this show is so well done. Krysten Ritter plays Jessica, and she's just this slip of a woman who is obviously trying to get the world to stop looking at her – but she can't stop looking at what's wrong in the world. You feel how damaged and deeply shattered Jessica is, and how much she just wants things to be simpler. And then she does these amazing things that make you understand that perhaps she's just trying to blend into the background because she's so extraordinary. Being uncomfortable with your abilities means that she doesn't want anyone to notice her, despite the fact that she deserves to be noticed and recognized.
Her friendship with Trish (played by Rachael Taylor) is also pretty great – it's got so much depth and history behind it. You can tell instantly how comfortable these women are with each other, and how much they love each other, with whatever complicated backstory that requires. Seeing the contrast of their lives – successful Trish who is a celebrity and constantly in the public eye versus Jessica, who just wants to slink into the background despite her hero abilities – is one the amazing parts of this story, and how they are so supportive of each others needs in spite of their differences.
The series also serves as the introduction of one of the next Marvel-Netflix television stars in Luke Cage played by Mike Colter. While none of the Jessica Jones characters made an appearance in Daredevil, we do get a connecting thread in the form of nurse Claire Temple (played by Rosario Dawson). So having the opportunity to get some real in-depth background on Luke before he spins off his own show is amazing. He and Jessica have a bit of a romantic relationship, and it's so much real-er and grittier than any comic book relationship I've ever seen on the screen. Their interactions are so raw, that sometimes it's almost hard to watch because it doesn't feel like the melodramatic fiction that we are so often presented on dramatic television, and especially with what's essential an action show.
The rest of the supporting cast are pretty terrific too, and I feel bad for not having the time to spotlight all of them, but I will just name Eka Darville as Malcome as particularly wonderful, and making such an amazing transformation both physically and emotionally over the course of the show.
So – would I recommend this show? Yes, definitely, but not to everyone. If you're the kind of person who likes bright and happy (sort of?) super hero movies that are feel goods where only the bad guys die and there is no real collateral damage, maybe steer clear. There are so many civilians who are victims in this show that it can feel difficult to watch. It's also surprisingly bloody, and I say this as someone who watched all the seasons of Hannibal. If you get queasy from blood or gory violence…this may not be your bag.
Otherwise, if you're willing to deal with the darkness and slight gore – do it. It's SO worth your time, and it was definitely worth mine.
Details: Jessica Jones, streaming on Netflix.
Movie – "The Danish Girl"
Recipe – Garlic Parmesan Kale Pasta
30-something professional librarian, writing about the things she loves.
Personal – An Uncertain Blog Future
Personal – An Update and Busy Busy Busy
Delay-Delay-Delay…Again
Theater – Oslo
Out and About – National Arboretum
a thing I made
DC Stuff
(step) mom stuff
breads + breakfasts