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The <tt class="xref py py-mod docutils literal">nova.scheduler.host_manager</tt> Module¶

Manage hosts in the current zone.

class <tt class="descname">HostManager</tt>¶

Bases: <tt class="xref py py-class docutils literal">object</tt>

Base HostManager class.

<tt class="descname">filter_hosts</tt><big>(</big>hosts, filter_properties, filters=None<big>)</big>¶

Filter hosts and return only ones passing all filters

<tt class="descname">get_all_host_states</tt><big>(</big>context, topic<big>)</big>¶

Returns a dict of all the hosts the HostManager
knows about. Also, each of the consumable resources in HostState
are pre-populated and adjusted based on data in the db.

For example:
{‘192.168.1.100’: HostState(), ...}

Note: this can be very slow with a lot of instances.
InstanceType table isn’t required since a copy is stored
with the instance (in case the InstanceType changed since the
instance was created).

<tt class="descname">host_state_cls</tt>¶

alias of <tt class="xref py py-class docutils literal">HostState</tt>

<tt class="descname">update_service_capabilities</tt><big>(</big>service_name, host, capabilities<big>)</big>¶

Update the per-service capabilities based on this notification.

class <tt class="descname">HostState</tt><big>(</big>host, topic, capabilities=None, service=None<big>)</big>¶

Bases: <tt class="xref py py-class docutils literal">object</tt>

Mutable and immutable information tracked for a host.
This is an attempt to remove the ad-hoc data structures
previously used and lock down access.

<tt class="descname">consume_from_instance</tt><big>(</big>instance<big>)</big>¶

Incrementally update host state from an instance

<tt class="descname">passes_filters</tt><big>(</big>filter_fns, filter_properties<big>)</big>¶

Return whether or not this host passes filters.

<tt class="descname">update_from_compute_node</tt><big>(</big>compute<big>)</big>¶

Update information about a host from its compute_node info.

class <tt class="descname">ReadOnlyDict</tt><big>(</big>source=None<big>)</big>¶

Bases: <tt class="xref py py-class docutils literal">UserDict.IterableUserDict</tt>

A read-only dict.

<tt class="descname">clear</tt><big>(</big><big>)</big>¶

<tt class="descname">pop</tt><big>(</big>key, *args<big>)</big>¶

<tt class="descname">popitem</tt><big>(</big><big>)</big>¶

<tt class="descname">update</tt><big>(</big>source=None<big>)</big>¶

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      Last updated on Thu Nov 1 16:11:51 2012, commit 4ed8d3e.
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!SLIDE bullets incremental

# Abstracting Data with Hiera #

* a place to put your data
* backend driven
* function call to lookup on keys
* hierarchy that is facter aware
* default and overrides

!SLIDE

## Public data in code

    @@@puppet
    class { 'jenkins::slave':
      jenkins_ssh_key => 'AAAAB3Nzbu84a....'
    }

!SLIDE commandline incremental

## Public data in code

    # cat /etc/puppet/hieradata/common.yaml
    ---
    jenkins_key: AAAAB3NzaC1yc2EAAAADA...
    ...

    # hiera -d jenkins_key
    DEBUG: Hiera YAML backend starting
    DEBUG: Looking up jenkins_key in YAML backend
    DEBUG: Looking for data source common
    DEBUG: Found jenkins_key in common

    AAAAB3NzaC1yc2EAAAADAQAB...

!SLIDE code

# Public data in code

    @@@puppet
    $ssh_key = hiera('jenkins_key')
    class { 'jenkins::slave':
        jenkins_ssh_key => $ssh_key,
    }

!SLIDE

# Secrets in code

    @@@puppet
    class { 'mysql::server':
        root_password => 'hunter2',
    }

!SLIDE commandline incremental

# Secrets in code

    # cat /etc/puppet/hieradata/common.yaml
    ---
    ...
    mysql_root_password: hunter2
    ...

    # hiera -d mysql_root_password
    DEBUG: Hiera YAML backend starting
    DEBUG: Looking up mysql_root_password in YAML backend
    DEBUG: Looking for data source common
    DEBUG: Found mysql_root_password in common

    hunter2

!SLIDE code

# Secrets in code #

    @@@puppet
    $password = hiera('mysql_root_password')

    class { 'mysql::server':
      root_password => $password,
    }

!SLIDE

# Conditional data in code

    @@@puppet
    class graphite {
      if $::osfamily == 'RedHat' {
        $pkgs = [
            'git',
            'python-django',
            'g++',
            'sqlite3',]
      ...
      }
    }

!SLIDE commandline incremental

# Hiera configuration

    # ln -s /etc/hiera.yaml /etc/puppet/hiera.yaml

!SLIDE commandline incremental

# Hiera configuration

    # cat /etc/puppet/hiera.yaml
    ---
    :backends:
      - yaml

    :yaml:
      :datadir: /etc/puppet/hieradata

    :hierarchy:
      - "%{clientcert}/common"
      - "osfamily/%{osfamily}/common"
      - common

!SLIDE

# Conditional data in code

    @@@puppet
    class { 'graphite':
        if $::osfamily == 'RedHat' {
            $pkgs = [
                'git',
                'python-django',
                'g++',
                'sqlite3',]
        ...
        }
    }

!SLIDE commandline incremental

# Hiera data

    # find /etc/puppet/hieradata
    .
    ./common.yaml
    ./osfamily
    ./osfamily/RedHat
    ./osfamily/RedHat/common.yaml
    ./osfamily/Debian
    ./osfamily/Debian/common.yaml

!SLIDE commandline incremental

# Hiera data

    # cat osfamily/Debian/common.yaml
    ---
    graphite::pkgs:
      - graphite
      - python-django
      - virtualenv

!SLIDE commandline incremental

# Hiera data

    # cat osfamily/RedHat/common.yaml
    ---
    graphite::pkgs:
      - git
      - python-django
      - g++
      - sqlite3
      - sqlite3-devel
      - python26-virtualenv

!SLIDE commandline incremental

# Hiera data

    # hiera graphite::pkgs  osfamily=RedHat
    ["git",
     "python-django",
     "g++",
     "sqlite3",
     "sqlite3-devel",
     "python26-virtualenv"]

!SLIDE commandline incremental

# Hiera data

    # hiera graphite::pkgs  osfamily=Debian
    ["graphite", "python-django", "virtualenv"]

!SLIDE commandline incremental

# Hiera data

    # hiera graphite::pkgs
    nil

!SLIDE

# Conditional data in code

    @@@puppet
    class graphite {
        if $::osfamily == 'RedHat' {
            $pkgs = [
                'git',
                'python-django',
                'g++',
                'sqlite3',]
        ...
        }
    }

!SLIDE

# Conditional data in code

    @@@puppet
    class graphite {
      $pkgs = hiera('graphite::pkgs')
      package { $pkgs:
        ensure => latest,
      }
    }

!SLIDE

# Conditional data in code

    @@@puppet
    class graphite (
      $pkgs,
    ){
      package { $pkgs:
        ensure => latest,
      }
    }

!SLIDE

# Backends #

* yaml, json
* file, ldap
* gpg, eyaml
* mysql, postgres, redis

!SLIDE

# Pros #

* Separation between data and code
* Composable classes via data bindings
* Backends, integration with existing datastores
* Some conditional logic irrelevant
* Puppet code sanitized

!SLIDE

# Cons #

* data bindings
* hard to figure out where things come from
* hiera-yaml can only support one data directory
* debugging
* public modules can't data bindings

!SLIDE

# In module data:

[puppet-module-data](https://github.com/ripienaar/puppet-module-data/)

!SLIDE

# Questions on Hiera

redpajama_set_name: RedPajamaGithub


text:
İnsel: an attack against democrats
Ahmet İnsel in Cumhuriyet writes that the October 10 attack was not perpetrated against Turkey. Neither was it made against the state or the AKP. The October 10 massacre was perpetrated against the forces of freedom, democracy and peace in Turkey. This was not a blind act of terror. Its victims were purposefully selected beforehand. Just as in Suruç and Diyarbakır, it was the people who are waging an unflinching, determined struggle for peace and democracy against the clique of Tayyip Erdoğan and the AKP's hegemony who were killed. The November 1 election is going to be a critical date, measuring both the determination to hold the power accountable and to stop those who want to drag us toward civil war. Every vote that is cast for the parties of the peace and freedom front on November 1 will be a slap on the face of those who perpetrated the October 10 massacre, who planned it, who hope to reap the benefits of the atmosphere of terror and chaos that they have created, and who are so satisfied with the sight of this barbarity that they are not even able to feign sorrow.
Ankara massacre
Gürsel: we recognize the signature of those who commissioned the Ankara massacre
Kadri Gürsel on the Diken news site notes that, interestingly, historically, assaults against the left, the Kurds, dissidents and democrats in Turkey are not stopped, and when they have been allowed to take place, they are never explained. That is not a coincidence. If an assassination in Turkey that could have been prevented still has taken place, you can rest assured that light will never be shed on it. The proprietor of the system of darkness in Turkey may change, but its signature mark that never changes is this. And, we recognize the origin of this signature that is left behind every unresolved assassination. The abbreviation may change. It was different in the seventies; in the 2015 it has become ISIS. The signature is always the same, though. That signature is written all over the bodies that were blown to death in Ankara. The purpose is to have society retreat within its shell, to make it fear for its future and to depoliticize it.
Cemal: I have no doubt – Erdogan's deep state is behind the terror
Hasan Cemal on the t24 news site writes that the government is fully responsible for the Bloody Saturday in Ankara. HDP co-leader Selahattin Demirtaş is the target of hellfire because he holds the state responsible for the Bloody Saturday. Stop there – as if the state in Turkey is innocent. Have you forgotten about all the provocations that were undertaken in order to pave the way for coups? Have you forgotten the massacres in Kahramanmaraş and Çorum in the 1970s? The evidence of how this state has violated the law in the name of its supposed superior interests are fresh in memory. There is not a single conspiracy that the "deep state" – whose roots go back a century – has not staged in this country in order to prevent democracy and the rule of law from being established. And I should also add this: the regime of Erdoğan, which for a while seemed to be signaling that it was fighting against the "deep state," has since it became the state itself, resorted to using he ominous instruments of the "deep state." About that, there is no doubt in my mind.
Gültekin: Islam and death
Levent Gültekin, who is a former Islamist and the author of a recent, bestselling book on the moral "defeat" of political Islam in power in Turkey, writes on the Diken news site that the first thing that comes to our mind when we think about the Islamic world is: death. We are not even afraid of saying, as we have done after the accident in which nearly one thousand pilgrims were killed, "How nice it was that these blessed people died at a blessed time, at a blessed place"! The religious understanding that prevails in the Islamic world is one that exults not in life, but death. It is because this religious understanding prevails that Muslims are utterly incapable of building cities that you can live in; it is because they have surrendered to this understanding that they do not bother to work on how to live a better life. In reality, as Muslims we are the living dead. That is because our lives have no value in these lands. And because death is valued more, things such as serenity, ethics, friendship, courtesy, knowledge, being principled – in short, to live like a human being – have no value. We cannot nourish any hope that we are ever going to be able to change this understanding, which prevents us from being like humans, and have it accepted by the Islamic world. So let us at least cease to preserve this religion – which is condemned to this understanding – as the focus of our lives.
Islam in Turkey

redpajama_set_name: RedPajamaCommonCrawl


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Toolbox – the nuts and bolts of writing, if you will – vocabulary, style, grammar etc.
Each of these sections is a treasure trove, but what struck me most when reading the book again (after a. being a professional writer for almost two decades and b. having been temporarily derailed from doing the thing I love by life) was that King presents us here with an incredible formula. In effect, he's saying that your writing equals the sum of who you are + your technical knowledge and writing ability + the person you are at the moment you start work on a piece of writing. I think that's pretty profound, and it's helped me get things in perspective and get everything working again.
Tagged On Writing: a Memoir of the Craft, Stephen King, Straight to You, What Works For Me.


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Searching for a job is a long arduous journey and if you're like me you often feel helpless, overwhelmed and hopeless. You apply and wait, you network and hope to hear back from contacts and at the end of the day you feel like you got nothing accomplished.
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That's a lot to take on, which is why I've started breaking down the process. Instead of thinking about all the hoops I'm going to have to jump through, I think about what I can do to either skip steps or what I can do to get me to step 6.
This phrase reminds me that everyday I need to do something, no matter how small to get me to that next position. Sometimes I blog or comment on someone else's blog, sometimes I network and sometimes all I have the energy to do is send out a few e-mails to friends and contacts asking about opportunities.
Searching for a career is a full time job and it's easy to become overwhelmed. When tackling something huge, like a career change, it's best to take baby steps. I know that I'm probably not going to be a full fledged graphic designer or CEO of a fortune 500 company tomorrow, but as long ask I keep working towards my goal of changing my career and improving myself, opportunities will present themselves and eventually I'll get to step number 6.
← Are you there Job? It's me, Amanda: Are you a good student?
I'm in the same position. I go between these extreme phases of not wanting to job hunt because I hate it so much, and then getting really inspired and dedicated to trying to move forward. This has created a great deal of emotional turmoil for me, but today is an "inspired" day, so I don't feel as down as I've been feeling lately. An unsuccessful job hunt feels depressing (actually, it IS) and it can be difficult to get back into the game. But I definitely agree that not only baby steps are important, but so is taking a break every once in awhile to reassess and take a breather.
One thing I did today was write down what I want to accomplish this week in regards to my job hunt and blog. Which I think will be really helpful in keeping me focused and accountable.


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Speaktoit Assistant uses Google's voice transcription service to understand your spoken commands. The app lets you choose from among several iSpeech voices, all of which sound a bit more natural than Google's robotic-sounding text-to-speech (TTS) service. I ended up settling on a female voice with a British accent that I found quite pleasant and easy to understand.
The app has a fun feature that allows you to design a custom avatar for your assistant. The avatar doesn't do anything functional, but it does give the app a bit more personality and puts a face on screen to go along with the app's voice.
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But even though Speaktoit Assistant is one of the best and most versatile voice-powered apps I've used, it had some hiccups and limitations. At times I had to repeat my commands multiple times or manually type them in, in order for Speaktoit Assistant to understand me. Also, the app could not read email or text messages aloud--a feature I had hoped would be included.
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Using this backup destination for accounts with a large number of files is not recommended. Due to DropBox API limitations, there is too much overhead for each file, which makes file transfers very slow.
We recommend using this for full compressed backups only.
Next step is to create a new app.
Go to your mail account and verify your email account via the email that you got from DropBox.
In the next screen, you will see the app/secret key.
Specify "Destination Type" as "Drop Box".
Fill in app/secret key that you have just created.
Click the "Request Access Code" button, for a Dropbox authentication code.
Copy the access code and paste it to the "Access Code" text area. Click the "save" button.
You can get an API app key by creating an app on the Dropbox's app creation page. Once you have Dropbox app created, the app key will be available on the Dropbox app's page (on the App Console).
You can get an API secret key by creating an app on the Dropbox's app creation page. Once you have Dropbox app created, the secret key will be available on the Dropbox app's page (on the App Console).
It's recommended to check the API call count to prevent backup job failures.


text:
Eucalyptus pachycalyx, commonly known as the shiny-barked gum, is a species of tree that is endemic to north-eastern Australia. It has smooth, pale grey, mottled bark, lance-shaped or curved adult leaves, white flowers and cup-shaped or hemispherical fruit.

Description
Eucalyptus pachycalyx is a tree that typically grows to a height of , often multistemmed or with a crooked trunk, and forms a lignotuber. It has smooth, shining, greyish bark with patches of yellow or orange new bark. Young plants have broadly lance-shaped to egg-shaped leaves that are  long and  wide. Adult leaves are the same shade of dull green on both sides, lance-shaped to curved,  long and  wide, tapering to a petiole  long. The flower buds are arranged in groups of seven, nine or eleven on peduncles, usually paired in leaf axils. The peduncles are  long, the individual buds on pedicels  long. Mature buds are oval,  long and  wide with a conical operculum. Flowering has been observed in February and the flowers are white. The fruit is a woody, cup-shaped or hemispherical capsule  long and  wide with the valves strongly protruding above the rim.

Taxonomy
Eucalyptus pachycalyx was first formally described in 1929 by Joseph Maiden and William Blakely in Maiden's book A Critical Revision of the Genus Eucalyptus. The specific epithet (pachyphylla) is from ancient Greek, meaning "thick" and "calyx", referring to the thick-walled fruit.

In 1991, Lawrie Johnson and Ken Hill described two subspecies of E. pachycalyx and in 1997 Hill described a third. The descriptions were published in the journal Telopea and the names have been accepted by the Australian Plant Census:
 Eucalyptus pachycalyx subsp. banyabba K.D.Hill;
 Eucalyptus pachycalyx Maiden & Blakely  subsp. pachycalyx;
 Eucalyptus pachycalyx subsp. waajensis L.A.S.Johnson & K.D.Hill.

Distribution and habitat
Shiny-barked gum grows in open forest on hills near granite outcrops. It occurs in isolated populations from the Atherton Tableland in Queensland, to far north-eastern New South Wales.

Conservation status
Subspecies banyabba is listed as "endangered" under the Australian Government Environment Protection and Biodiversity Conservation Act 1999 and under the New South Wales Government Biodiversity Conservation Act 2016. Subspecies waajensis, which only occurs in Queensland, is classified as "endangered" under the Queensland Government Nature Conservation Act 1992.

References

pachycalyx
Myrtales of Australia
Flora of New South Wales
Flora of Queensland
Trees of Australia
Plants described in 1929
Taxa named by Joseph Maiden
Taxa named by William Blakely

redpajama_set_name: RedPajamaWikipedia


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Prozac Nation is a American drama film directed by Erik Skjoldbjærg, starring Christina Ricci, Jason Biggs and Anne Heche. It is based on an. Biography . Jessica Lange was mentioned in the "Prozac Nation" book. When Elizabeth is clearing the table near the end of the film, she clears away the. The self-centered brat at the center of Prozac Nation spends most of her time making life miserable for everyone around her, but there's little reason the public .
26 Jan I probably discovered the movie adaptation of Elizabeth Wurtzel's autobiography, Prozac Nation, in a lethargic haze. I was depressed at the. 10 Sep The brat at the center of "Prozac Nation" makes life miserable for everyone Reviewed at Toronto Film Festival (Special Presentation), Sept. Prozac Nation was a good movie C. Ricci I have to give her some props she has proven to be a very good actress I've seen her in a few movies and she always.
14 Jun Dismissed by critics at the Toronto Film Festival, Erik Skjoldbjærg's adaptation of Elizabeth Wurtzel's Prozac Nation was also given a pass. 11 May Not many may have seen the Christina Ricci-headlined film adaptation of Prozac Nation. But it's a film with a real power to it. 1 Mar That's true of depression, and it's definitely true of actress Christina Ricci's portrayal of the disorder in the movie, Prozac Nation. Whatever its flaws, one can certainly not accuse Prozac Nation of avoiding risks. The movie, directed by Erik Skjoldbjaerg (the original Insomnia) and adapted. 9 Dec When in the film version of Prozac Nation, Christina Ricci's Lizzie goes on Prozac , we are pleased not so much for her but rather all those.
9 Nov Article on debate over delays in releasing film version of Elizabeth Wurtzel's memoir Prozac Nation, memoir of clinical depression; photo. 17 Jul Ten years after it was published, Elizabeth Wurtzel's Prozac Nation is still Surely, then, the film version, with a stunning central performance. Those of you who were waiting so impatiently for the release of this movie will be as sorry as Besides Harvard, Prozac Nation gives writing itself a bad name. 29 Aug Overlooked really is the first adjective I'd ascribe to my choice for this week's Overlooked Movie, "Prozac Nation". Based on Elizabeth Wurtzel's.
Elizabeth is on the verge of losing her grip on life after she leaves her emotionally fraught home to start college. Quickly, her life takes a turn for the worse: She. 5 Jul Today's entry comes from our favorite studio for this particular genre, Miramax and the movie is Prozac Nation, based on the best-selling novel. quotes from Prozac Nation: 'That's the thing about depression: A human being can survive almost anything, as long as she sees the end in sight. But d. Prozac Nation, Erik Skjoldjaerg's film adaptation of Elizabeth Wurtzel's best- selling memoir about depression, is an emotionally charged drama about.


text:
Associate Editor, Business & Free Speech America
Against the Tide: MyPillow CEO Mike Lindell Refuses to Pull Ads from Tucker Carlson Tonight
In a country where corporate liberal pandering to violent mobs is the new trend in business, one CEO decided to stand on principle and go against the tide.
MyPillow CEO Mike Lindell has refused to join companies like T-Mobile in pulling advertising from Fox News's Tucker Carlson Tonight following Carlson's rebuke of the leftist Black Lives Matter movement. Lindell told Newsweek: "'MyPillow is not changing its advertising. I make all my advertising decisions based on what is best for my customers and my employees.'"
Lindell said that "'MyPillow believes all lives matter and values all our employees and customers, treating them like family.'" Newsweek noted that Lindell's "company is a major advertiser on Carlson's show, with 45 airings between May 27 and June 9, according to iSpot.tv."
Carlson dared to say of the Black Lives Matter movement during the June 8 segment of his show: "This may be a lot of things, this moment we're living through, but it is definitely not about black lives." Carlson continued: "[R]emember that when they come for you, and at this rate, they will."
Watch Carlson's powerful segment on Black Lives Matter below:
Such sentiment caused companies like "Disney, Papa John's and T-Mobile" to pull their advertising from Carlson's show, Newsweek noted.
T-Mobile CEO Mike Sievert responded to a random Twitter user questioning the company's sponsorship of Carlson. Sievert said: "We aren't running ads on that show and we won't be running ads on that show in the future. Bye-bye, Tucker Carlson!"
Sievert also responded to another Twitter user who threatened to cancel his T-Mobile service if the company supported Carlson. Sievert even made sure to hashtag his support for the leftist movement and restated an earlier-made taunt: "Bye-bye, Tucker Carlson!"
Meanwhile, T-Mobile is still running ads on liberal shows such as CNN Tonight With Don Lemon, CNN's Cuomo Prime Time, CNN's Anderson Cooper 360 and TBS's Full Frontal With Samantha Bee.
Corporate Liberalism
Tucker Carlson Tonight


text:
\section{Introduction}
Dengue Fever is a mosquito-borne disease with a significant global burden. Half of the world's population and 129 countries are at risk of infection \cite{brady2012refining}. Between 100 and 400 million infections are registered each year globally \cite{bhatt2013global}.  Dengue presents as a flu-like illness with symptoms ranging from mild to severe. There are four serotypes, meaning up to four infections are possible for each person over their lifetime, with potentially life-threatening complications arising from severe Dengue. There is no specific treatment or universal vaccine, and the primary preventative measure is vector control. This underscores the importance of disease surveillance. Predictive models can help efficiently allocate public health resources to combat Dengue, with the goal of reducing the overall disease burden \cite{who2020dengue}. Dengue incidence is associated with many different risk factors. These include climate conditions such as rainfall, extreme weather events, and temperature \cite{chien2014impact, thu1998effect, fan2013identifying}, land use \cite{kilpatrick2012drivers}, and poverty \cite{mulligan2014dengue}. These factors may have nonlinear, context-specific, and time-variant effects on disease incidence, which poses a challenge to disease modeling.

The literature on Dengue forecasting is multi-disciplinary, uniting expertise from areas such as epidemiology, environmental science, computer science, and mathematics. Modeling frameworks include both theoretical and data-driven approaches. Compartmental models, for example, estimate the dynamics of Dengue infections in a population over time, and are based on extensive knowledge of the host, vector, and transmission process. Another simulation technique, agent based modeling is useful for estimating impacts of interventions, such as the release of sterile males to control the mosquito population \cite{isidoro2009agent}. Statistical time series forecasting takes a more data-centric approach and is effective at modeling the highly auto-correlated nature of Dengue Fever \cite{johansson2016evaluating, riley2020sarima_preprint}. Machine learning models leverage the increasing data availability on risk factors of disease spreading and offer non-parametric approaches that require less detailed knowledge of the disease and context \cite{guo2017developing}.

Neural networks are a subset of machine learning algorithms, which have made significant contributions to medicine and public health, including applications such as medical image analysis for disease diagnosis \cite{qayyum2018medical}, identifying abnormalities in signals such as electrocardiographs (ECG) \cite{papik1998application}, and optimizing decisions of health care providers, hospitals, and policy-makers \cite{shahid2019applications}.
Neural networks are also used to forecast diseases, including Malaria \cite{zinszer2012scoping}, Influenza. \cite{alessa2017review}, and Covid-19 \cite{bullock2020mapping}.

This review examines the use of neural networks for Dengue Fever prediction. The objective is to summarize the existing literature and also provide an introduction to this still somewhat novel modeling technique. Our contributions are as follows:
\begin{itemize}

    \item We summarize the technical decisions made in the literature, including architecture selection and hyper-parameter tuning.
    \item We examine the data inputs (such as climate or population demographics) and best predictors of Dengue fever identified in specific contexts.
    \item We review the relative performance of different neural network architectures and comparator models, such as other machine learning techniques.
\end{itemize}

To the knowledge of the authors, no systematic review of the literature on neural networks applied to Dengue Fever prediction has yet been conducted.
Siriyasatien et al (2018) \cite{siriyasatien2018dengue} provide a broader review of data science models applied for Dengue Fever prediction. Racloz et al (2012) \cite{racloz2012surveillance} review the literature on surveillance systems for Dengue Fever. The study finds that most papers use logistic or multiple regression to analyze Dengue risk factors. (Seasonal) Auto-Regressive Integrated Moving Average (S/ARIMA) models have become a popular choice to incorporate auto-regressions but are not suitable for all data types. Though also apt at analyzing auto-regressive behavior, the study does not review any literature on neural networks or machine learning for Dengue surveillance. We aim to fill this knowledge gap in the present review.

\section{A Primer on Neural Networks}

This section gives an overview of some of the neural network models that are relevant to Dengue prediction. We explain the model first intuitively, then mathematically, and focus on feed-forward neural networks, which are used most often for Dengue prediction. Many high-quality textbooks and open source tutorials offer a deeper introduction to neural networks (for example \cite{deng2014deep, haykin1999neural}). Several Python libraries include easy-to-use implementations of neural network models (for example TensorFlow \cite{tensorflow}, scikit-learn \cite{scikit-learn}, or Keras \cite{chollet2015keras}). We also provide sample code for Dengue prediction in a Github repository\footnote{https://github.com/KRoster/NN4Dengue}.

Like other machine learning models, neural networks learn to execute specific supervised tasks, such as predicting the number of Dengue infections, based on a large set of labeled examples. Given a set of inputs, such as climate conditions, the model learns to estimate the output, such as next month's Dengue incidence. After making a first guess, the model looks at the correct answer and updates its parameters to reduce its error on the next iteration until its predictions are optimized.

Computationally, neural networks are represented as a network of processing units ("neurons" or "hidden units") that transform and pass information from one layer to the next. During training, we distinguish between forward- and backward-propagation, depending on the direction of the information flow. During the forward-propagation step, input information passes between units, each time being transformed according to a set of weights and a non-linear activation function. The prediction error of the output relative to the true label is computed. Back-propagation is then used to reduce this error on the next iteration: The weights of each unit, the parameters that define how information is combined, are updated according to their influence on the prediction error. This combination of forward- and backward-propagation is repeated several times until the predictions are increasingly accurate.

After this training phase, model performance is tested on a hold-out test set. Since the test set is not used to train the model, it can give a good indication of how well the model will generalize to new data, once applied in the real world.

\begin{figure}[!h]
    \centering
    \includegraphics[width=0.9\textwidth]{"nn_fully_annotated".png}
    \caption{Sample feed-forward neural network architecture}
    \medskip
    \small
    The figure shows a sample neural network architecture with 4 input features, 2 hidden layers with 5 and 2 nodes respectively and a single node in the output layer.
    \label{fig:nn annotated}
\end{figure}

There are many different neural network architectures that are designed for different kinds of inputs and tasks. The architecture determines how the units are connected, how information flows through the network, and how many weights need to be optimized. An example of a feed-forward network is illustrated in figure \ref{fig:nn annotated}. It shows the forward propagation of information through the individual neurons of the network, which was described above. The information that is passed through a given neuron $j$ in layer $l$, called its activation $a_{j}^{[l]}$, is computed as the linear combination of weights $w_{ij}^{[l]}$, biases $b_{j}^{[l]}$, and inputs from the previous layer $a_{i}^{[l-1]}$, which is passed through a nonlinear activation function $g^{[l]}$:

\begin{flalign} \label{eq forward prop}
    a_{j}^{[l]} &= g^{[l]}(\sum\limits_{i=1}^{n_l} w_{ij}^{[l]} a_{i}^{[l-1]} + b_{j}^{[l]}) \\
    \text{with: } a_{j}^{[0]} &= X_{j} \\
    \hat{y} &= a^{[k]}
\end{flalign}

\noindent where ${n_l}$ is the number of neurons in layer $l$, $k$ is the number of layers (the input layer is counted as layer $0$), and $\hat{y}$ is the prediction that is generated in the final layer.

The gradient of the loss with respect to a given weight $w_{ij}^{[l]}$ or bias  $b_{j}^{[l]}$  tells the model how the given parameter needs to be adjusted, with the learning rate $\alpha$ determining the size of the adjustment:

\begin{flalign} \label{eq gradient descent}
    \theta &= \theta - \alpha \frac{d\mathcal{L}}{d\theta}
\end{flalign}

\noindent where $\theta$ is a given set of weights or biases to be updated and $\mathcal{L}$ is the loss.

A benefit of neural networks is that parameters are updated by the model itself, not predefined by the researcher. They do not rely on strong assumptions and knowledge of the disease. Yet some hyper-parameters of neural network models must still be set by the researcher and are generally determined through a combination of domain knowledge and iterative experimentation. These hyper-parameters are tuned using a validation set or through cross-validation. Hyper-parameters may include the learning rate (by how much the weights are updated at each backward propagation step), the number of epochs (how often the forward and backward propagation steps are repeated), the mini-batch size (how many training examples are processed at each step), how weights are initialized (for example all zeros or random values), the number of hidden units in each layer, and the number of layers. Other important decisions include the choice of activation function in each layer, the choice of loss function, and the relative size of training, validation, and test sets. The optimal choices and ranges tested for these hyper-parameters depend, among other aspects, on the size and type of data available and the nature of the predictive task. One aim of this review is to summarize the choice of parameters deemed optimal by researchers in the existing literature to assist future researchers in the hyper-parameter tuning process.

Besides feed-forward neural networks, this review includes two other categories of models used for Dengue forecasting. Recurrent neural networks (RNN) were developed for the analysis of sequence data, such as time series (sequences of observations in time) or text (sequences of words). They are cyclical (they contain loops) and process sequences of inputs together, giving the network a "memory" to remember historical feature states. RNNs are used in contexts where the evolution of input features matters for the target feature, where the prediction relies on information at multiple time steps. An example is machine translation, where the meaning of a word is influenced by its context, by the other words in the sentence. The number of Dengue cases may also depend on the sequence of risk factors or the sequence of previous Dengue incidence. Using time series data, RNNs may help capture the auto-correlation of the disease and lagged influence of risk factors, such as rainfall or travel patterns. Within the family of RNNs we distinguish different architectures, such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) \cite{hochreiter1997lstm, schmidhuber2015deep, lipton2015critical}.

Convolutional neural networks (CNN) are another subclass of deep neural network models with applications in computer vision \cite{lecun1990handwritten}, such as image segmentation and classification. Inputs into CNNs are three-dimensional - such as a two dimensional image with three color channels, which form a 3-D matrix of pixel values. The architectures of CNNs include different kinds of operations, such as convolution and pooling, which help identify key regions in the image that help make the final prediction. For example, the model may learn that the presence of a body of water (shiny pixels in a satellite image) influence the number of Dengue cases we should expect. Crucial in this example is that the type of relevant landscape feature is not defined by the researcher but learned by the model. However, CNNs may also be used for data processing prior to prediction, for example to classify land use (such as vegetation or urban areas) from satellite images, which are known to influence Dengue spread and which can then be fed into Dengue a separate predictive model.

The tasks executed by machine learning models in general, and neural networks in particular, fall into two different categories: classification and prediction. Classification is the process of assigning a categorical label to an example based on its inputs, such as determining whether a patient has Dengue fever or a different disease. Prediction is the process of forecasting a future state based on historical or current inputs, for example predicting next month's Dengue incidence from this month's rainfall.

This review is focused on predictive models of Dengue Fever at the population level. In addition to classic regression models, we include papers using classification for prediction approaches, which produce categorical instead of continuous outputs. For example, a model may predict whether a city will experience an outbreak (binary classification) or which risk category will prevail (multi-label classification).

\section{Methodology}

\begin{figure}[!h]
    \centering
    \includegraphics[width=0.8\textwidth]{"PRISMA_2009Flow_filled_new".pdf}
    \caption{PRISMA Flow Chart}
    \label{fig:prisma flow}
\end{figure}

This review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement guidelines \cite{prisma2009}. The PRISMA flow chart is represented in figure \ref{fig:prisma flow}. A systematic search was conducted using Web of Science/ Knowledge, Scopus (abstract, title, keywords), PubMed, and Science Direct (abstract, title) databases. References of papers appearing in the search results were also examined for relevant works. The searches were conducted between 21 July and 17 August 2020 and used the following search string:
\begin{center}
    "( deep learning OR neural network ) AND dengue"
\end{center}

\begin{comment}
The databases yielded the following numbers of results:

\begin{itemize}
    \item Web of Science / Knowledge: 92 (abstract screened: 46) (full text review: 26)
    \item PubMed: 21 (abstract screened: 4) (full text review: 4)
    \item Scopus: 23 (abstract screened: 18) (full text review: 8)
    \item Science Direct: 13 (abstract screened: 3) (full text review: 3)
    \item references of other papers: 31 (abstract screened: 27) (full text review: 21)
\end{itemize}

\end{comment}

The papers were examined for inclusion in the review in three stages: by title, by abstract, and finally by full text. In each phase, the following inclusion and exclusion criteria were applied liberally to determine which papers would be considered in the next phase. If the information in the title and/or abstract was inconclusive, the paper was included in the full-text review. Inclusion and exclusion criteria are as follows:

\begin{itemize}
    \item Studies must implement a neural network or deep learning technique, either as the main method or as a comparator model. Reviews of the literature on neural networks for Dengue forecasting would also be considered.
    \item Studies must predict Dengue fever incidence or risk. There are no restrictions on the type of target variable used. For example, the number of Dengue cases, Dengue incidence rate, or a binary Dengue risk variable are all accepted as target features.
    \item Studies must examine Dengue in a human population. Models for disease diagnosis of individuals are excluded. Studies modeling the location of vectors without relation to Dengue incidence are excluded. Studies examining animal hosts are excluded.
    \item As the search query is in English, only English language articles were identified and included in the review.
\end{itemize}

\section{Results}
This section summarizes the findings of the review. The full list of papers and their properties are provided in table 1 at the end of this section.

\subsection{Prediction Target}
Within the criteria of study selection, there was significant variation in the study specifications, including the formulation of the target feature. Most studies predict the future number of Dengue cases based on historical time series. However, there are some notable exceptions. Andersson et al (2019) \cite{andersson2019combining} predict a static incidence rate at the neighborhood level in Rio de Janeiro, Brazil. Two studies predict the risk of a Dengue outbreak: Abeyrathna et al (2019) \cite{abeyrathna2019scheme} formulate their task as a binary classification of 'outbreak' or 'no outbreak', while Anno et al (2019) \cite{anno2019spatiotemporal} classify cities according to five risk categories.

Early forecasts provide more time for public health response but may have lower confidence. In most of the selected studies, researchers predict one period ahead corresponding to the measurement frequency of the data used. A single study predicted the present: Livelo and Cheng (2018) \cite{livelo2018intelligent} use social media activity in the Philippines to predict the present situation of Dengue infection. They use a neural network model to classify tweets according to a set of different categories of Dengue-related topics. Their weekly Dengue index based on this Twitter data is correlated with actual Dengue case counts. Koh et al. (2018) \cite{koh2018model} compare one and two-week forecast horizons. Soemsap et al. (2014) \cite{soemsap2014forecasting} use a two-week horizon with weekly data inputs. Dharmawardhana et al (2017) \cite{dharmawardana2017predictive} predict Dengue cases in Sri Lankan districts four weeks ahead using weekly data on cases, climate, human mobility, and vegetation indices.
Chakraborty et al. (2019) \cite{Chakraborty2019forecasting} use the longest forecasting horizon. They use three different datasets of Dengue cases, two of which are weekly (Peru and Puerto Rico), and one which is measured monthly (Philippines). For the weekly data, they compare three and six month horizons. For the monthly data, they compare a horizon of six months with one year forecasts. For Peru and the Philippines, the best model changes with the prediction horizon.

\subsection{Data Sources}

\begin{figure}[!h]
    \centering
    \includegraphics[width=0.9\textwidth]{"articles_for_review_variables_used2".pdf}
    \caption{Variable types used in the selected studies}
    \medskip
    \small
    The figure illustrates how frequently studies included individual data types as well as combinations thereof. Each row represents a data type and the horizontal bars measure their frequency of inclusion in the selected papers. The columns represent the combinations of variables, and the vertical bars measure how often the given combinations occurred.
    \label{fig:variables}
\end{figure}

Besides epidemiological data on the history of Dengue Fever, which is used in all studies, the most common predictors are meteorological variables (used in 14 studies) (see figure \ref{fig:variables}). Additional data sources include: vegetation indices and other landscape features extracted from satellite images (in two studies), human mobility, specifically mobile phone data (used in one study), aerial and street view images (in one study), social media data, specifically from Twitter (one study), vector information (one study), and demography (one study). Three papers use only epidemiological data.

\begin{figure}[!h]
    \centering
    \includegraphics[width=0.8\textwidth]{"articles_for_review_distribution_of_geographic_scale".png}
    \caption{Geographic granularity used in the selected studies}
    \label{fig:geographic granularity}
\end{figure}

Most studies examine granular geographic regions, such as cities (nine studies) or districts (six studies).  Two studies even modeled Dengue at the sub-city (neighborhood) level (see figure \ref{fig:geographic granularity}). Granular scales can be beneficial in two ways: they allow for local predictions and accordingly targeted public health response. They also generally result in larger training datasets with more observations than national or state-level aggregations, which plays to the strength of neural networks. The studies cover countries in both Asia and Latin America, which are considered the primary Dengue endemic regions \cite{who2020dengue}. A total of 13 countries are included (see figure \ref{fig:countries map}), with the Philippines and Brazil appearing most often.

\begin{figure}[!h]
    \centering
    \includegraphics[width=0.9\textwidth]{"articles_for_review_geographic_distribution".png}
    \caption{Countries covered by the selected studies}
    \label{fig:countries map}
\end{figure}

\subsection{Model Selection}

\begin{figure}[!h]
    \centering
    \includegraphics[width=0.9\textwidth]{"articles_for_review_models_used2".pdf}
    \caption{Models used in the selected studies}
    \label{fig:models}
    \medskip
    \small
    The figure illustrates how frequently studies used different forecasting models as well as combinations thereof. Each row represents an algorithm and the horizontal bars measure their frequency of inclusion in the selected papers. Green rows refer to neural network models, while blue bars show comparator models, such as machine learning algorithms. The columns illustrate which combinations of models were used, the frequency of which is measured by the vertical bars.
\end{figure}

Most studies implement a shallow neural network with just one hidden layer. Four studies use deeper feed-forward architectures. Three studies employ recurrent neural networks, two of which used LSTM units and one used a GRU. Three studies implemented CNNs.

The neural network models were compared to a range of other models. Support Vector Machine (SVM) models were most common (six studies). Generalized Additive Models (GAMs), (S)ARIMA and linear autoregressive (AR) models occurred twice each. The remaining comparators were used in just one paper each: Gradient Boosting Machine (GBM), XGBoost, Linear regression, Poisson regression, Tsetlin Machine (TM), Cellular automata, a compartmental model, and a naive baseline. Figure \ref{fig:models} shows how frequently different model types and combinations thereof were used in the selected literature.

The most common comparator model used is SVM. Evidence suggests that both SVM and neural networks perform well, and which is better depends on the context. In Xu et al (2020) \cite{xu2020forecast}, an LSTM neural network outperforms an SVM model in predicting the number of Dengue fever cases in 20 Chinese cities. Transfer learning further increased the performance gains in cities with few overall Dengue cases. Abeyrathna et al (2019) \cite{abeyrathna2019scheme} use a classification for prediction approach to identify whether districts in the Philippines are likely to experience a Dengue Fever outbreak. Whether SVM or the neural network model is better in the context of this study depends on the evaluation metric used: SVM has a higher precision, while ANN has a higher F1-score. Yusof and Mustaffa (2011) \cite{yusof2011dengue} and Kesorn et al (2015) \cite{kesorn2015morbidity} both find SVM to have a higher performance than the neural network.

Four studies use a feed-forward network with at least two hidden layers. They provide varying evidence to the performance relative to shallow neural networks or other machine learning models. In \cite{dharmawardana2017predictive}, a model with two hidden layers performs better than an XGBoost model in forecasting Dengue in Sri Lankan districts. In two studies (\cite{abeyrathna2019scheme} and \cite{baquero2018dengue}), the deep neural network provides better predictions than alternative neural network models, but is not the best model overall: In \cite{baquero2018dengue}, the MLP with two hidden layers performs better than the LSTM-RNN to forecast Dengue in a Brazilian city, but a GAM is best overall. In \cite{abeyrathna2019scheme}, a model with three hidden layers has lower errors than models with a single or five hidden layers. However, the best F1 score in classifying binary outbreak risk is achieved by a Tsetlin Machine. The final study \cite{rahayu2019prediction} uses a model with three hidden layers but does not employ any comparator models.

Performance of the RNN models is also mixed. The two studies using LSTM-RNNs produce contradicting results despite having similar study designs. Baquero et al. (2018) \cite{baquero2018dengue} and Xu et al. (2020) \cite{xu2020forecast} both use LSTM-RNNs to forecast Dengue at the city level, compare their model to a feed-forward neural network and a GAM, use meteorological and epidemiological input features, and evaluate performance using RMSE. Xu et al. (2020) \cite{xu2020forecast} find the LSTM to have the best performance relative to all comparator models, whereas in the study by Baquero et al. (2018) \cite{baquero2018dengue}, the LSTM-ANN performs worse than the simple neural network and the best model overall is the GAM.

The three studies using CNNs do not provide comparisons to other types of models, though one study compares different CNN architectures. However, the key advantage of the examined CNN studies is the granular geographic resolution in predictions. The models take satellite and other images as inputs and can therefore generate forecasts at the sub-city level. Andersson et al. (2019) \cite{andersson2019combining} compare different kinds of CNN architectures (based on DenseNet-161) for each of three input types, specifically satellite imagery, street view images, and the combination of the two. Their model predicts Dengue incidence rates at the neighborhood level in Rio de Janeiro, Brazil. Best performance is achieved when combining street and aerial images. Rehman et al. (2019) \cite{rehman2019deep} implement a pre-trained CNN (based on U-Net architecture) to extract landscape features from satellite imagery, which they use in a compartmental SIR model to forecast Dengue in neighborhoods of two Pakistani cities. Anno et al. (2019) \cite{anno2019spatiotemporal} use a CNN (based on AlexNet architecture) to classify five levels of outbreak risk in cities in Taiwan, based on images capturing sea surface temperatures.

\subsection{Architecture}
Architectures vary across the different models. The studies implementing shallow models with one hidden layer cumulatively tested 1-30 hidden units. Three studies (\cite{wijekoon2014prediction, aburas2010dengue, soemsap2014forecasting}) tested a broad number of values across this range, and their optimal number of hidden units were related to the number of input features. Two studies \cite{wijekoon2014prediction, aburas2010dengue} identified four hidden units as the optimum. Both used meteorological and epidemiological variables and both measured four different pieces of information. Wijekoon et al (2014) \cite{wijekoon2014prediction} used temperature, rainfall, humidity and Dengue cases. Aburas et al (2010) \cite{aburas2010dengue} included a total of seven features that measured the same information but with additional lags. The third study \cite{soemsap2014forecasting} identified 25 nodes as the optimum, which matches the larger input size of 32 features. Instead of trying different values, Datoc et al (2016) \cite{datoc2016forecasting} fixed the number of hidden units to the number of input features, and selected the best-performing model among eight models with three to five nodes. The best model used three nodes (corresponding to three input features).

Abeyrathna et al (2019) \cite{abeyrathna2019scheme} compared shallow and deep architectures. Their best neural network model had three layers with (20, 150, 100) units respectively. This model performed better than the two shallow models with five or 20 neurons in a single hidden layer. It also outperformed a five-hidden layer model of (20, 200, 150, 100, 50) topology.

Baquero et al (2018) \cite{baquero2018dengue} also used deep architectures with 2 layers with combinations of (10 or 20) neurons in the first layer, and (5 or 10) neurons in the second. They implemented both MLP and LSTM models. The best neural network model was an MLP with (20,10) neurons, though a Generalized Additive Model (GAM) and an ensemble both performed better than the neural networks.

The other LSTM model was implemented by Xu et al (2020) \cite{xu2020forecast} with a different architecture. They used a single hidden layer with 64 neurons. Unlike Baquero et al (2018) \cite{baquero2018dengue}, their model outperformed GAM as well as SVR and GBM.

\subsection{Model Evaluation}
The studies utilize a range of metrics to assess model performance, in part due to different prediction targets. The most common measures are Root Mean Squared Error (RMSE) and Pearson's correlation, which are each used in seven studies each. Mean Average Error (MAE), Mean Squared Error (MSE), and accuracy are also commonly used.

\section{Discussion}

The literature on neural networks applied to Dengue forecasting is still somewhat scarce, but the reviewed studies include some promising examples, where neural networks outperform other machine learning or statistical approaches. The literature therefore suggests that neural networks may be appropriate for inclusion in a set of candidate models when forecasting Dengue incidence. However there is variation as to whether neural network models or other approaches perform best. Future research may compare different model types and architectures on multiple datasets, to better understand how the ideal model varies by context, such as geography or data availability.

Though many risk factors of Dengue have been identified, most neural network models limit inputs to meteorological and epidemiological data. Future studies may evaluate the value of alternative predictors, especially as NNs tend to deal well with high-dimensional problems. Along the same lines, there is space for more incorporation of non-causal predictors of Dengue fever from sources such as social media. Some papers in this review have leveraged these new predictors, specifically from Twitter \cite{livelo2018intelligent}, Street view and satellite images \cite{andersson2019combining}, mobile phone records \cite{dharmawardana2017predictive}, and features derived from satellite imagery \cite{dharmawardana2017predictive, rehman2019deep}. These data sources have also been effective with other machine learning models, for example Baidu search data to predict Dengue in a Chinese province \cite{guo2017developing}.

Transfer learning was implemented in \cite{xu2020forecast}, where it provided promising results. The authors trained a model on a city with high Dengue incidence and used it to predict disease in lower-incidence geographies. This was the only study in this review that used transfer learning outside the context of pre-trained CNNs. This may present another avenue for further research, especially in locations where data is scarce.

\subsubsection*{Acknowledgments}

Kirstin Roster gratefully acknowledges support from the São Paulo Research Foundation (FAPESP) (grant number 2019/26595-7). Francisco Rodrigues acknowledges partial support from CNPq (grant number 309266/2019-0) and FAPESP (grant number 2019/23293-0).

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redpajama_set_name: RedPajamaArXiv


It's in the deep application and understanding of something of interest to you that true insight, innovation and ideas lie.
Your child who trusts the system, is repeatedly told to toil hard and put hours and hours of effort into meeting expectations is cheated out of one of the most precious gifts the human brain has which is to be curious and ask questions to learn.
The education system with its focus on learning from textbooks and regurgitating that information in exams takes away from children the power to delve deep and explore to come up with more questions of their own, make multiple connections, figure out answers themselves and make contextual meaning and sense out of the content they discovered in the process.
As I interact with children across age groups at Sparkling Mindz through our Young Achievers Program, through the TED-Ed Clubs, I see a shift in the attitude towards learning. Most of them trivialize learning, want to run away from it, don't think it is useful or meaningful in their lives and those who put in hours of effort too only know how to figure out the answers for questions in the text book so they could excel in exams. When you run on the treadmill of exams there is little time for anything else.
The world, however, has higher expectations from your child. The world expects your child to discover and nurture their uniqueness, develop an opinion, a voice that they can use to stand up for not only themselves but the community and world at large, to solve problems bigger than themselves and how are you training your child to develop these skills?
The classic moment was recently when I asked a bunch of children, "What do you learn English for?" pat came the answer, "for exams" and after a moment of silence I heard one of our Young Achievers voice, eyes shining, she said, "for communication." and the room lit up in awe. I would think every child should have thought of and known the answer to that question as most of them learn the English language for well over 16 years as a subject!
Another interesting conversation I had with a 14 year old. He was quite angry with the fact that his mother thought that video games were a waste of his time. He loved to play them and saw a future in the industry. However, according to the mother not wrongly so, no exams, no marks, no future so he should study and keep the video games aside for a while. He took it up as a challenge to prove to her that video games were not so bad. It took him a while to research the pros and cons, articulate what he felt, figure out what to use/discard from the research and eventually change his stance to "too much of anything is bad" and pick up and analyze examples of learning from games that he liked. Much of that too was researched data, the child did not yet know/understand how to add a layer of own insight into the research. Diving deeper into topics results in children discovering their own interests, passion and a sense of identity like nothing else does. Like everything else in life, it is a matter of choice to empower your child with the skills to go deep and discover themselves or skim the surface of learning where everyone else remains too.
At Sparkling Mindz, it's our mission to nurture and develop children's curiosity and make them confident learners.


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/ Maggie Koerth-Baker / 9 am Tue, Oct 11 2011
Space dust: Your tax dollars at work
By Maggie Koerth-Baker
Your tax dollars build bridges. They pay the salaries of teachers and firefighters. Tax dollars help put people through college, provide a safety net for the elderly and the disabled, and pay for fighter jets and nuclear bombs.
You may not agree all those ways your tax dollars are spent, but they are all, at least, fairly tangible. When it's time for re-election, your senator can point to a roads project, a school, a saintly grandmother, or a missile silo. Through these projects, Americans are being educated, cared for, and protected.
But it's hard to make that clear cost/benefit analysis for basic scientific research. At least, not on a timetable that matches up with election cycles.
Basic research is often weird, and it's often boring. It's the years spent mapping the neurons of zebra fish, so that future scientists can have a more detailed biological model to work with. It's the chemical analysis that has to happen, so that two decades from now somebody else can discover a new cancer-fighting drug. Basic research is about curiosity, and knowledge for knowledge's sake. By it's very nature, basic research relies on public funding. But by it's very nature, it's hard to explain how the public benefits from the basic research we fund.
Attila Kovacs is one of the scientists who put your tax dollars to work. An astrophysicist at the University of Minnesota, he specializes in the study of space dust. That is, yes, dust. In space. It's the sort of thing that would be very easy to mock. (Imagine Bill O'Reilly making a joke about lemon-scent space Pledge.) But Kovacs says space dust matters more than you think. And he makes a good case for why it's important to spend tax dollars on funny-sounding science.
Maggie Koerth-Baker: You study space dust, but what does that really mean? Is this the same thing as dust on Earth, just in space? Or is space dust something a little different than the stuff that builds up on our bookshelves and end tables?
Attila Kovacs: In some ways it is similar. I like to think of Earth as a giant dust ball. Earth was made of space dust, but it went through a lot of evolution so dust that's on Earth now isn't exactly the same. We don't actually know the structure of space dust, but we can guess. It probably has metallic core surrounded by a carbon or silicate shell and an ice mantel. They may be shaped like snow-flakes or a crumpled piece of paper. And we know that a typical speck of space dust is about 0.1 microns, about 1 thousandth of the width of your hair. It's hard to get your hands on space dust. We can only get indirect evidence through observation, by looking at the light that goes through the dust.
For instance, we know the size of space dust because light that has a wavelength larger than the particles of dust has come through the dust. We can see how different wavelengths of light either get blocked or go through the dust layers and we can put a size on that.
But what really makes dust interesting to me is its intricate connection to star formation. Dust is produced by stars in their dying phase, and it's also an essential ingredient for making new stars and planets. Interstellar dust is mostly heated by massive young stars less than a million years old. In fact, most of the light from stars is absorbed and re-emitted as heat by dust. So, by measuring the heat contained in dust we can get an accurate picture of the current level of star-formation in galaxies at all ages of the Universe. Through the dust, we can directly measure the complete star-formation history of the universe and get a glimpse at when and how the galaxies and stars came into being. This is what I research.
Yet another interesting aspect of space dust is its role in the chemistry of space. Most molecules, including molecular hydrogen, water, CO, and even some organic compounds that we see in space, have formed on the surfaces of dust grains, which act as catalytic surfaces enabling chemical reactions at the low temperatures and densities of space. There is no other way to make such molecules. All the precursor organic molecules of life on Earth probably formed on dust grains around a dying star, before our Sun and solar system were even born.
A scanning electron microscope image of an interplanetary dust particle. CC licensed, via Wikipedia.
MKB: When did you decide to dedicate your life to studying space dust?
AK:
I was drawn to astronomy from a very young age. Soon after I learned to read, my grandmother took me to a bookstore, and told the clerk to get me whatever book I wanted. I told him I wanted a book about astronomy. The clerk got me something appropriate for a child of age 6, with pretty drawings of a smiling Sun and all, but I was very upset. I told him I wanted something much more serious. In the end, we settled on a book that would be your college-level intro astronomy. I loved it. I did not understand it all, but I still loved it. Later my interests turned to physics. But physics was a pretty dry landscape. A lot of the really crazy discoveries go back to the beginning of 20th century. Astronomy is always new and exciting. Every time you turn on a new telescope, there's always something new you'll discover. That's what drew me back to astronomy.
As for dust, dust is the most prominent thing that you will see in space, even more than stars and star-light. More than half of all the light from galaxies in the universe is radiated as heat from dust grains. It's also the most critical ingredient in the chemistry of the interstellar medium.
MKB: I'm assuming you're not the only person studying this stuff. What makes your approach different? What aspects of space dust are you looking at that your colleagues aren't?
AK: To some degree all of us are doing somewhat different work, but that doesn't mean there's not overlap. But I think it's important to have that overlap. That's where you get the credibility of science. Without that
there's no way to check whether somebody is right or wrong. Redundancy is a cross check.
What I personally do different: We do the same sort of observations. When I get observing time to look at a few galaxies on a telescope, there are people doing similar observations. But what's unique about what I do is the models that I use for analyzing the data and the tools I develop. Most astronomers who observe similar subjects are really users of technology. They use what's there. I, on the other hand, try to think about what will be the next gadget we can bring to the telescope that will enable us to do this research even better. I don't know a lot of people in the "dust" community that do that.
For example, I've worked to develop the equivalent of digital cameras for this long wavelength light, that lies between the infrared and radio bands. Essentially, they're very sensitive thermometers. When you put them on telescopes then the light from the distant galaxy heats up the detector and you notice this very small temperature change. The instruments I helped to build are used on telescopes in Hawaii, Chile and Spain. And more recently I had an interesting idea on how to build an instrument that would split that light into 1000 different colors for each pixel, and then you can take pictures of both the dust and the dominant chemistry in galaxies. You can get a vast amount of information from that because you will be able to detect and map dozens of molecular lines in distant galaxies all at once. I'm hoping to build such an instrument and get it and on a telescope in a few years.
MKB: That sounds expensive. How do you fund this research? Who funds it, and how does that process work for you?
AK: We're relying a lot on government agencies, particularly the NSF (National Science Foundation) or NASA. And there are two ways to get funding. First is through regular grant projects, which are 3-year cycles where you apply for a grant to do specific research. And NASA also provides funding to use their space telescopes. So you can propose to do a specific bit of science with them and if you get observing time then they'll give you some money to help you with that.
The process starts with a proposal. You tell them what you want to do, why it's important, and what you hope to learn. You really have to justify your work. They don't just give you money because it's nifty. The grants are peer reviewed. And your peers decide whether it merits funding or not. They look at what you've done before with funding. They look at the potential impact, and what you'll do to communicate your science to the public. This is how they select who gets the funding. Typically it's for a 3 year cycle. Every 3 years your whole life hangs in the air. And it's far from guaranteed. Most things I apply for, 1 out of 20 or 1 out of 100 proposals are successful. It's far from easy.
Pig at the Minnesota Tax Cut Rally 2011, a Creative Commons Attribution (2.0) image from fibonacciblue's photostream.
MKB: How expensive is your work? When you do win these grants, what does the money go toward?
AK: At the bottom level it's the salary. I get around $40,000 and when you add overheads and whatnot that the university pays, it's maybe $70,000. To do the science we have to build the instruments. We can't just use our eyes on the telescope, and those tools typically cost a few million to develop.
Then there's the telescopes themselves. A 10 meter radio telescope up on some high mountain, that's maybe $10 to $15 million to build, and a few millions a year to run. Private donors often pay for the construction of telescopes that will bear their name — like the famous Keck telescopes in Hawaii, paid for by W. M. Keck. A space telescope can be upwards of $1 billion.
That's expensive. But you have to think of what it costs to the typical taxpayer. With just a single dollar of tax you pay every year, how much can you buy when it's combined with the $1 everyone else pays? For $1 per person every year, you're going to pay for 2000 post-doc researchers. That's 2000 people like me. Or you could buy up to 100 instruments to put on the telescopes. Or your $1 can also buy you a few telescopes a year or one space telescope every few years.
MKB: You do basic research, stuff that's really driven by curiosity, not by short-term practical goals. In a time of tight budgets, why is that important? In a recession, is space dust really something we can afford to spend money on?
AK: These are difficult times and when the money is tight we have a tendency to ask whether this is practical, and do we really need it. The answer to that is that it's not the need that drives discovery. It's the other way around: discovery drives our needs. You can only need things that you already know about. What basic science does is look for new knowledge. What it will be made of in the future, we don't know. But often it brings us new things that will be very practical.
My favorite example is electricity. Electricity, when it was discovered, wasn't anything useful. But once it's discovered, then you can start thinking about how you'll use it. What you have to think about is this: What is practical is something very short term. Basic science is much more forward looking. Some of it will produce useful things 10 to 30 years from now.
If you're tight on your own budget how do you trade off on everyday necessities versus saving for retirement? You can't pay for rent and food at the cost of not saving at all for retirement. So when the budget is tight you have to cut back on both ends, rather than eliminating one. And we should do that in science. You can't sacrifice the future because times are bad now.
MKB: Okay, but is there something you can look at and say, "This is what people will get if they fund me?" Is there anything tangible?
AK: For my work specifically? Well, you never know where it will take you. There are some things you can foresee a little bit. But I'm hesitant to guess. You do get benefits from astrophysics, though. There's the selfish curiosity of knowing, but in that process we create technologies to detect the light we're analyzing. And a lot of those will be practical in the future.
When you're using your digital camera today, you're using technology that optical astronomy developed 30 years ago. Today it's in your camera. Lots of technologies from radio astronomy are in your cellphone today. You wouldn't have that without basic science trying to detect light at different wavelengths. You really have no idea where the technologies we develop today will take you in another 10, 20 or 30 years.
MKB: But why government funding? Why not find a private organization or corporation that wants to help you research space dust?
AK: I think there's many sides to this. Right now, private funding tends to be product based. It has to lead to a product within a few years for a private company to be interested. So it may be practical and possible for things with immediate applications, but it's hard for me to see why a corporation would be interested in something long term and uncertain.
Basic research used to be privately funded in the past, like with Bell Labs. That used to be THE place where basic research was happening. But somehow that model has disappeared and I think it's because corporations are looking for more short term goals. There's really no corporation doing basic research in the same way Bell Labs did.
But there are also reasons why you might not want it, even if they were interested. Corporations are interested in proprietary technologies and getting out ahead of another company. They won't share what you discovery and they'll use it exclusively to their advantage. They'll file patents and protect their turf. And that's fine. But the reason we want public funding is that we want to generate public knowledge. We want to share this with the world. We want it to be immediately available to everyone around us. Science doesn't have trade secrets. I think public funding is essential to keep it that way.
Editorial note: This interview is part of a blog carnival on publicly funded science. Organized by Annalee Newitz at iO9, science writers around the Web have produced stories showcasing the triumphs of publicly funded research. You can read Annalee's post that started it all.
And, if you're so inclined, please consider contacting your congressperson about the importance of public funding for science. There's a budget proposal due out on November 23rd, which promises to slash funding to organizations like the National Science Foundation, the National Institutes of Health, and the National Oceanographic and Atmospheric Administration. Whether you realize it or not, those institutions have had a major impact on your health and your quality of life. As Attila Kovaks says, fiscal responsibility is important. But we won't solve this country's money problems by cutting off our source of future innovation.
Main Image: Cosmic dust of the Andromeda Galaxy as revealed in infrared light by the Spitzer Space Telescope. Public domain, via Wikipedia.
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Australia fires: Air-dropping veggies to feed wallabies [NEW VIDEO]
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Video: 5 California mountain lions hanging out together, home surveillance camera footage
This is some pretty amazing and highly rare video — seldom do you get footage of five, count 'em FIVE, California mountain lions all hanging out together. The big cats were captured on home surveillance video, in a rare gathering of the typically solitary critters.


I moved with my parents and three siblings to New Orleans from the Midwest three years ago. I grew up in a really affluent town, so school was the most stressful thing going on in most kids' lives. That wasn't the case for me. I lived in 19 different houses growing up. For a while it was like two houses per year, so moving around was pretty commonplace. But one of the hardest parts was that I would come home from school some days and all my stuff would just be packed up. They'd be like, "Oh, we're moving," and there was no forewarning. But at least we had somewhere to go.
When I was 14, my family became homeless. It was the first time we didn't have a place. The first night we slept in our storage unit, then we lived in hotels for like a couple of weeks until we moved in with my grandparents for a summer - the summer between the 8th grade and my freshman year. It was stressful. I became more inward. I didn't talk to very many people and I became a weird child. I was socially awkward and didn't know how to interact with people. I didn't wanna talk about "normal' things people talk about because it wasn't relevant to me and seemed stupid. It impacted my grades, too. I never wanted to pay attention in school. When you're dealing with issues at home, you don't feel like certain things are as important. So I felt that school didn't matter as much as the other things on my mind and I never paid attention.
There was a lot of tension between my parents and my grandparents. it kind of ruined the vibe of grandma's house. I didn't really talk to counselors because my parents taught us not to talk about it. I felt like it was wrong to say something because in their minds, if we talked about it being a problem it meant they were bad parents - but they weren't. They're great parents. I did have one good teacher that helped me out. I think it's a lot of teachers' job to be friends to the kids who don't have any friends. My older sister helped me, too. At the time I didn't want to listen to her and it felt like she was being controlling. Now I realize she was just stepping in as a parental figure. I even think she's a better adult because of it and she's really on top of things.
But yea, it's hard to make friends when you don't feel like anyone wants to be your friend. I ended up switching schools my freshman year, to a smaller, more alternative school. I met some people who really liked me and helped me build my confidence. After that I was able to make more friends. It helped me figure out how to sort through everything, too. I'm always afraid that I'm going to grow up and end up being as unstable as my parents were. That's something I'm really scared of. So I just tell myself that things are okay and that I'm not my parents. We're completely different people. Even though parts of them are me, that doesn't mean all of me is them. I don't have to make the same choices they did.
Looking back, I would have wanted more support from my parents and for my teachers to understand that not everyone's home life is super easy. My parents still feel guilty about what happened so I don't bring it up because they'd feel like I'm accusing them of being bad parents. But I'd never do that. I just want to know what exactly was going on when I was a kid. Why didn't we have enough money? What went wrong?
So now, at 18, I have a plan. I'll stay in school forever! My dad didn't get a Bachelor's degree and my mom just completed hers and I see how it affected their lives. I also won't have kids early. That's something my parents did that I don't want to do. So far, so good. My life is a lot more stable in New Orleans and I really like it here. Growing up I mostly interacted with White people, so it was a bit of a culture shock at first. But now I feel well-adjusted and I have a wider worldview than before. I stay busy with two after-school programs that are all about making a positive impact on the world, and I'm taking a class at Delgado Community College as a senior in high school. My parents both have jobs and my mom even went back to school and finished her Bachelor's degree. My stress as a kid came from the uncertainty of having a place to live, so now, everything is a lot better.


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Case study - Lillian and John Spence, Benidorm
"It doesn't matter if the value of our apartment falls further. We're at an age where we just want to enjoy life, and that is more important than it being a good investment"
For Benidorm regulars, Lillian and John Spence, putting in a cheeky offer for an apartment they'd coveted for three years has made a lifetime dream come true, reports Lucy Moore
At first sight, the Spences from Belfast thought the Spanish resort of Benidorm was too built-up and high-rise. They'd travelled
All over the world, and liked Lanzarote in particular, but thought they'd try the well-known resort in Alicante on the Costa Blanca.
But once they'd discovered its more authentically Spanish old town - and the beaches - it began to grow on them, and they started visiting three or four times a year, always to the same apartment, in the Levante beach end of the new town.
During that time, they watched an apartment block being built close by, and began to think about buying somewhere for themselves, says Lillian, 64, who admits she was much more enthusiastic than her husband john, 68.
"Every time we visited Benidorm, I'd take john to see the apartments, trying to persuade him that we should consider buying," recalls Lillian, pictured above. "He was unprepared to take the risk - until we looked at the second block of the development, which was even nicer than the first, last February. And the prices began to be reduced.
"Initially priced at €210,000 (£175,000) for a 15th-floor one-bedroom apartment, we then saw them priced at €140,000 (£116,000) and after seeing a vacant one with the most amazing views of sea and mountains, we both thought we had to go for it. So we put in a cheeky offer of €110,000 (£91,000) and were actually astonished that the bank accepted it - plus, we offered to pay cash. The process was completely unfamiliar, so we were helped through it by Robert Evans at Basico homes [which specialises in bank-owned properties: www.basicohomes.co.uk). He put us in touch with a lawyer as well as suggesting we use a currency-exchange broker to make the most of our pounds."
The Spences completed in June and have no regrets so far. "Savings are not worth it these days, so we thought - what with our sons being secure financially - we might as well use the money to buy," says Lillian.
"We were really familiar with the area, we'll save money on renting for several weeks each year, and eventually prices will recover in Spain. "You might as well enjoy life. As we say in Northern Ireland, there are no pockets in a shroud..."
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