text:
Computer Fluid Dynamics
Light Electric Vehicles
Servotest Systems
Events, Conferences & Awards
Energy audits and certificates
CFD Simulation
Wind Tunnel Testing
Design and Construction of Wind Tunnels
Eco-mobility
Servotest Test & Motion Simulations Systems' Sales Agents in Spain and Portugal
R&D in new projects
KEELWIT PERFORMS THE DESIGN AND COMMISSIONING OF AN AERODYNAMIC TESTING WIND TUNNEL FOR THE MOTOR & SPORT INSTITUTE
by Keelwit Technology | 1 Apr 2018 | Computer Fluid Dynamics, Light Electric Vehicles, Wind Tunnels
KeelWit has designed and is currently manufacturing a tailored wind tunnel for the new facilities of the Motor and Sport Institute. This wind tunnel for aerodynamic testing will allow to analyze the aerodynamic performance of scale vehicles and compare it with the results obtained by Computational Fluid Dynamics using the most advanced software. Wind tunnel sensors and measuring equipment will provide a practical knowledge about aerodynamics for the MSI's students that is not achievable in other centers where there this type of equipment is not available. Furthermore, Teo Martín Motorsport team will use the wind tunnel up to 200 km/h to test the aerodynamics settings of its vehicles with which the team races successfully on different categories. Other motorsport teams would use the MSI facilities to fine-tune their cars, due to the small number of available wind tunnels for testing with these...
Computer Fluid Dynamics, Light Electric Vehicles, Wind Tunnels
Isaac Prada presents the application of the MOST-HDS algorithm, combined with CFD, to the design of objects and devices, in a webinar organized by ANSYS USA and by GOMPUTE
by Keelwit Technology | 11 Dec 2017 | Computer Fluid Dynamics
KeelWit applies its proprietary Multi-Objective Structured Hybrid Direct Search (MOST-HDS) methodology, which introduces optimization algorithms in the exploration of the most suitable combinations of the values of the different variables of highly complex functions, to reach the optimal value of the target function in CFD projects. By applying this methodology, KeelWit has obtained important improvements in the selected target function within the design of wind tunnels, water slides, injectors, exhaust gas ducts, etc. In the webinar Isaac explained how the use of this methodology can be considered a quantum leap when designing components, as it allows to apply ongoing phases of optimization over hundreds of possible combinations of the values of the variables, when compared to the usual methodology of selection and testing of 3 or 4 different configurations, almost exclusively based in the know-how obtained from previous designs. The joint use of GOMPUTE's cloud High Performance Computing and ANSYS simulation software has allowed KeelWit to introduce improvements % in the target function that have ranged from 18% to 77% in device design for different...
KeelWit attends the "Innovation Day" organized by ANSYS Spain
by Keelwit Technology | 30 Nov 2017 | Events, Conferences & Awards
Three of KeelWit Technology's engineers attended the different presentations made throughout the day, being able to see and debate the new features of the numerical simulation tools of ANSYS, as well as the "best practices" of other Spanish companies in areas as different as space rockets, mobile phones or microalgae...
Commissioning a wind tunnel for aerodynamic testing
by Keelwit Technology | 10 Nov 2017 | Light Electric Vehicles, Wind Tunnels
In order to equip itself with the most modern technology and to become a reference center among educational institutions in the area of racing cars' engineering, a new center that will open its doors in 2018 has hired KeelWit to design and manufacture its wind tunnel for aerodynamic testing. Once the specifications list of the device has been agreed, KeelWit is applying its MOST-HDS algorithm to optimize its design while respecting the available footprint in the room where it will be installed, as well as the speed indexes that are required for accurate...
Light Electric Vehicles, Wind Tunnels
KeelWit article in the scientific magazine DYNA on CFD optimization of boilers for combined cycles
by Keelwit Technology | 17 Oct 2017 | Computer Fluid Dynamics
KeelWit has published an article titled Boiler inlet duct shape design optimization for combined cycle power plants. Founded in 1926, DYNA is one of the most influential and prestigious engineering journals in the world, as recognized by Thomson-Reuters in the annual edition of its JCR report and undoubtedly a reference of scientific publications in Spanish. The contribution of this paper is twofold: on the one hand, it shows that there is substantial room for improvement in the shape design of the inlet ducts of Heat Recovery Steam Generators (HRSGs), in terms of achieving a lower pressure drop, a higher velocity uniformity and an important cost reduction of the unit; on the other hand, it shows how the application of the Multi Objective Structured Hybrid Direct Search (MOST-HDS) algorithm, can find improved designs. This algorithm is applicable in many fields for aerodynamic shape optimization involving big displacements, which can be quite unconventional and non-intuitive. The MOST-HDS algorithm combines genetic, gradient and swarm search intelligence in every iteration. The results obtained for the two HRSG families presented show that there are optimum trade-off design points with simultaneous reductions in pressure drop of up to 20- 25%, in lateral surface of up to 38% and in boiler inlet length of up to 16%, while having comparable velocity uniformities to the existing designs. Link to open access article:...
MOTOR AND SPORT INSTITUTE TRUSTS KEELWIT FOR A 7 POSTER SERVOTEST MACHINE
KEELWIT CELEBRATES ITS 7TH ANNIVERSARY
Computer Fluid Dynamics Related news
Energy Efficiency Related news
Events, Conferences & Awards Related news
Light Electric Vehicles Related news
Other Projects Related news
Servotest Systems Related news
Wind Tunnels Related news
KeelWit Robust & Dreamlike Technology


Oh my, I bet Crian Whiskey is more than a little liquifying!
Getting drunk in a place like that is ill advised. That said, I've been hammered in places I would never go sober.
Of course, that would be the LAST mistake those people would make, but still... Would NOT get Dolly to Lynn I think.
Oh do l detect a nod to Scotty from the old original Star Trek series? Alien and Mr. Scott getting way drunk, as they drink their way through the bar. They come across a bottle even Scotty isnt familiar with. The alien asks "whats this?" Scotty staggers and squints at it... "its...its...its green!"
And appropriate. The title of that episode was "By Any Other Name". The A Rose was implied but not actually in the title.
Scotty didn't get enough screen time, but when he did, it was always worth it!
One of my favorite bits is from the movies. And sadly, am choking on which one, tho a niggling in my brain wants to say Undiscovered Country, where he has an exchange with Uhura, and after she leaves with a warning to be careful, he mutters to himself not to worry about him, he knows this ship like the back of his hand. And walks his noggin into a bulkhead going thru a port. Out cold.
Not out cold .. just holding his head and sighing ..
That was from The Final Frontier. My favorite ST movie. That was also my favorite scene.
James T. Kirk: Mr. Scott, you're amazing.
He did knock himself completely out. I just rewatched the scene.
Don't forget Relics, when Scotty comes back for TNG. "I was drivin' starships while your great grandfather was still in diapers."
I miss James Doohan. I do.
Hah! Yes it was, I caught the ref as well. Awesome!
oh I think it will be amusing .. for us at least.
She stretches, looks over him... "It's Green!!!"
"I know... I think you broke it."
And now Scotty is actually going where no human has gone before. When he died he was the first human to be "buried" in the depths of space his casket was lifted aboard an outgoing probe and launched toward the center of the Universe as we know it. Gene Roddenberry followed suit when he died.
Not sure if was first. First attempt, rocket failed. The company that does those ash launches, only put them in LEO. Wont even make it to the sun. The ashes will eventually renter and burn up like a wee meteor. Or maybe more then wee. Guessing the launch multiple tubes of ashes in a larger container. Doubt they the eject all the tubes. They got enough debris up there to keep track of with out deliberately adding more. Be nice if they did those launches on trajectories that would take em to the Sun. We are star dust, after all. Be nice to go back, reincorporated, and eventually with our star, be ejected into the universe at large when it starts dying and making it's planetary nebula. Least that's what *I* want.
PS: Just had a mem trigger. Can't recall who it was, but on one of the lunar probes some years back, that was looking for water in polar craters. That two part one. (an impactor followed by a sensor suite that also impacted after flying thru the ejecta plume of the first looking for water signatures)I seem to recall one of them had ashes from somebody aboard. Was that good ol Scotty? Or someone else? Or am I getting trivia mixed up in the mash I call my brain again?
Is Crian Whiskey also known as LPR?
Lower riser package, equipment used for well intervention on a subsea oil well.


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Suggestions for other agronomy fact sheets? Email Quirine Ketterings (qmk2@cornell.edu).
These fact sheets reflects the current (and past) authors' best effort to interpret a complex body of scientific research, and to translate this into practical management options. Following the guidance provided in this fact sheet does not assure compliance with any applicable law, rule, regulation or standard, or the achievement of particular discharge levels from agricultural land.


The four Little Lou children's story and coloring books are refreshing stories about a little black labrador's lessons in life. In this book, Little Lou learns the importance of loving others and putting them first. Little Lou is reminded that selfishness can never bring happiness, but unconditional love many times results in joy.


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text:
Q: hide frame border behind another frame xamarin.forms i am trying to do something like this:
this is the code i wrote:
MainPage.xaml:
<?xml version="1.0" encoding="utf-8" ?>
<ContentPage xmlns="http://xamarin.com/schemas/2014/forms"
             xmlns:x="http://schemas.microsoft.com/winfx/2009/xaml" xmlns:gradient="clr-namespace:Syncfusion.XForms.Graphics;assembly=Syncfusion.Core.XForms"
             x:Class="ALNahrainAlphaApp.MainPage"

             >
    <ScrollView Orientation="Vertical">
        <Grid >

            <gradient:SfGradientView>
                <gradient:SfGradientView.BackgroundBrush>
                    <gradient:SfLinearGradientBrush>
                        <gradient:SfLinearGradientBrush.GradientStops>
                            <gradient:SfGradientStop Color="White" Offset="0.0" />
                            <gradient:SfGradientStop Color="#89cff0" Offset="1.0" />
                        </gradient:SfLinearGradientBrush.GradientStops>
                    </gradient:SfLinearGradientBrush>
                </gradient:SfGradientView.BackgroundBrush>
            </gradient:SfGradientView>
            <StackLayout>
            <Label  Text="AL-Nahrain" TextColor="Black" FontSize="Large" FontAttributes="Bold" FontFamily="audiofont" HorizontalOptions="End" Margin="0,50,50,-15" />
            <BoxView HorizontalOptions="FillAndExpand" Color="Black" HeightRequest="2" Margin="0,0,10,0"/>

            <Label Text="App" TextColor="Black" FontSize="Large" FontAttributes="Bold" FontFamily="audiofont" HorizontalOptions="End" Margin="0,-12,15,0"/>

                <Frame CornerRadius="50" HeightRequest="100" WidthRequest="100" HorizontalOptions="Center" VerticalOptions="Center"  Padding="-1"  BorderColor="Black"  Margin="20,0,20,-80">
                    <Image HeightRequest="150" WidthRequest="150" x:Name="alNahrainLogo"  HorizontalOptions="FillAndExpand" VerticalOptions="FillAndExpand"/>
                </Frame>
                <Frame BackgroundColor="Transparent" BorderColor="Black" CornerRadius="50" Margin="20,20" >
                    <StackLayout>
                <Frame Margin="0,40,0,0" BackgroundColor="Transparent">
                <StackLayout  >
                        <Label Text="Username" TextColor="Black" FontSize="16" Margin="25,0,20,0" x:Name="emailAnimate" />
                    <Entry Margin="20,-40,20,0" x:Name="email" />

                </StackLayout>

            </Frame>

            <Frame Margin="0,-10,0,0" BackgroundColor="Transparent">
                <StackLayout  >
                        <Label Text="Password" TextColor="Black" FontSize="16" Margin="25,0,20,0" x:Name="PassAnimate" />
                    <Entry Margin="20,-40,20,0" x:Name="password" IsPassword="True"/>
                    <ImageButton Source="hidePass.png" WidthRequest="25" HeightRequest="25" HorizontalOptions="End" BackgroundColor="Transparent" Margin="0,-40,20,0"  x:Name="show"/>

                </StackLayout>

            </Frame>

                <Button CornerRadius="20" Text="Login" FontSize="Medium" BackgroundColor="#3d3d3d" TextColor="White" Margin="50" FontFamily="audiofont" x:Name="login" />
                <Button CornerRadius="20" Text="Register" FontSize="Medium" BackgroundColor="#3d3d3d" TextColor="White" Margin="50,10" FontFamily="audiofont" x:Name="register" />
                <StackLayout Orientation="Horizontal" HorizontalOptions="Center" VerticalOptions="Center" Margin="30">
                    <Switch  HeightRequest="40" WidthRequest="50" OnColor="White" ThumbColor="Black" x:Name="rememberme" IsToggled="True"/>
                <Label Text="Remember Me" TextColor="Black" FontAttributes="Bold" Margin="10"/>

            </StackLayout>
                    </StackLayout>
                </Frame>
            </StackLayout>

        </Grid>
    </ScrollView>
</ContentPage>

but i couldn't find a way to hide the border of the big frame behind the circular frame. this is what i obtained:

i tried to use a grid but i got the same result. how can i hide the border when the circular frame is above it? thanks in advance.

redpajama_set_name: RedPajamaStackExchange


text:
Welcome to Montana No Kid Hungry!
Connecting Kids to Healthy Food
We make it easier for kids to access wholesome meals so they can learn, play, grow and dream.
AMPLIFY MONTANA
Hunger is a symptom of poverty, and it is only when everyone has a seat at the table that we will be able to truly end hunger in our communities.
More Info Please
Montana No Kid Hungry
Nearly 1 out of every 6 kids in Montana will struggle with hunger during their childhood. Our mission is to end child hunger in Montana by connecting kids to the healthy food they need to succeed in the communities where they live.
Through a partnership with the Department of Public Health and Human Services (DPHHS), the office of Montana Governor Steve Bullock, and the national non-profit organization Share Our Strength, Montana No Kid Hungry has dedicated itself to putting an end to child hunger in the state.
Montana No Kid Hungry is working to eliminate childhood hunger in Montana using a three-pronged approach:
1). Improve food access by increasing participation in school breakfast, summer meal, and afterschool meal programs.
2). Enhance meal quality at school and at home, keeping the focus on wholesome foods.
3). Raise awareness through increasing the visibility of this important issue, getting communities involved, and engaging youth.
REMEMBERING MLK
"There is nothing new about poverty. What is new, however, is that we have the resources to get rid of it." Dr. King, Nobel Peace Prize address, 1964
MY FAVORITE HOLIDAY...
Thanksgiving! And why it's more important than ever before.
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VISTA Steven recounts all his adventures over the past month.
MONTANA IN THE SPOTLIGHT
Watch this VIDEO about Browning, MT produced by Share Our Strength.
START MAKING A DIFFERENCE
Montana No Kid Hungry partners with AmeriCorps VISTAs through the Prevention Resource Center - READ MORE
Engage with us to make no kid hungry a reality - join THE MAIN COURSE today!

redpajama_set_name: RedPajamaCommonCrawl


Once there log in, then type in 883 for the QuickLaunch code. You will have to enter a name, please just use your first name and your last initial.
Watch the 4 short videos, and take notes while you watch. This should take at most 15-20 minutes.


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text:
PSLS Originals
PSLS • News • PS4 News, Trophies, Reviews, and More
CD Projekt RED Has Confirmed That Cyberpunk 2077 Won't Be Censored in Australia
Zarmena Khan Saturday, May 02, 2020
CD Projekt RED has confirmed to GameSpot that Cyberpunk 2077 has received classification by the Australian rating board and will not be censored for players in the country.
The Australian Classification Board is known to refuse ratings to video games that contain content the authorities deem inappropriate (drug use, for instance), and require developers to edit content before their games receive a rating. However, Cyberpunk 2077 didn't have to go through that and managed to earn an R 18+ classification, which is considered "high impact." The rating mentions Cyberpunk 2077's use of sex, violence, and its references to sexual violence.
Preorder Cyberpunk 2077 for the PlayStation 4
"Receiving an age rating in Australia was an important milestone on our way to releasing the game in September," CD Projekt RED's Kuba Kutrzuba told GameSpot. "We're creating a mature title for a mature audience, so we were expecting R 18+. We haven't had to alter the game in any way and Australian gamers are set to enjoy the full Cyberpunk 2077 experience."
That's certainly good news for CD Projekt RED, which had to edit The Witcher 2: Assassins of Kings for Australia in order to receive a green light from the rating board. The studio's John Mamais previously expressed concerns about the possibility of a refusal but hoped that the context of the controversial content will be considered.
Cyberpunk 2077 will release on September 17th for the PS4, Xbox One (and Series X), Google Stadia, and PC.
[Source: GameSpot]
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monitoring_string = "e648000e5cd42cece065ea6b2f880692"


We all know that attitude is everything, but at Mt. Rose altitude is everything. With a base elevation of 8,260 our parking lots are higher than the summits at many other areas, ensuring the best conditions in the basin, even on the warmest spring days. Carve, bash bumps or simply cruise on nearly 1,000 acres and almost 1,500 vertical feet of bowls, trees and glades. New this year, more terrain features for every ability level from gentle rollers to extreme peaks.
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25 minutes from Reno makes Mt.Rose the closest major resort to an international airport in the world. So, get out your boards, come up to Mt. Rose and discover a whole new altitude.


text:
export default class clients {
  static GoogleMapsClient: GoogleMapsClient;
}

export class GoogleMapsClient {
  autocomplete(object: any): any;
  geocode(object: any): any;
  placeDetails(object: any): any;
}

redpajama_set_name: RedPajamaGithub


Our final speaker of the year will be Miho Takeuchi, who will give a talk and demonstration on "The Art of Sashiko Stitching". Miho is a Japanese Sashiko artist and designer who now lives in the US. Her website is designbyaika.


text:
module Rouge
  module Lexers
    load_lexer 'jsx.rb'
    load_lexer 'typescript/common.rb'

    class TSX < JSX
      extend TypescriptCommon

      title 'TSX'
      desc 'TypeScript-compatible JSX (www.typescriptlang.org/docs/handbook/jsx.html)'

      tag 'tsx'
      filenames '*.tsx'

      prepend :element_name do
        rule %r/(\w+)(,)/ do
          groups Name::Other, Punctuation
          pop! 3
        end
      end
    end
  end
end


text:
\section{Introduction}

In this publication, we study the directed graph embedding problem in an unsupervised learning setting.
A graph embedding problem is usually defined as a problem of finding a vector representation $X \in \mathbb{R}^K$ for every node of a graph $G=(V, E)$ through a mapping $\phi\colon V \rightarrow X$.
On every graph $G=(V,E)$, defined with set of nodes $V$ and set of edges $E$, the distance $\operatorname{d}_G\colon V \times V \rightarrow \mathbb{R}_{+}$ between two vertices is defined as the number of edges connecting them in the shortest path, also called a graph geodesic.
In case that $X$ is equipped with a distance metric function $\operatorname{d}_X\colon X \times X \rightarrow \mathbb{R}_{+}$, we can quantify the embedding distortion by measuring the ratio $\operatorname{d}_X / \operatorname{d}_G$
between pairs of corresponding embedded points and nodes.
Alternatively, one can measure the quality of the mapping by using a similarity function between points.
In contrast to distance, the similarity is usually a bounded value $[0,1]$ and is in some ad-hoc way connected to a notion of distance e.g. inverse of the distance.
Usually, we are interested in a low-dimensional ($K \ll n$) embedding of graphs with $n$ nodes as it is always possible
to find an embedding~\citep{Linial1995} with $L_{\infty}$ norm with no distortion in an n-dimensional Euclidean space.
Bourgain theorem (1985) \cite{Bourgain1985} proved that it is possible to construct an $O(\log(n))$-dimensional Euclidean embedding of an undirected graph with $n$ nodes with finite distortion.

For the last couple of decades, different communities such as machine learning~\citep{hamilton2017representation,MFembeddings,NeuralHyperbolic, DeepWalk, node2vec},
physics~\citep{Kriukov, HyberbolicPhysRevE, Cannistraci2017}, and computer science~\cite{Landmarks2009, Landmark2006} independently from mathematics community~\cite{Linial1995, Johnson1986, Frankl1988, Bourgain1985} developed novel graph representation techniques.
In a nutshell, they can be characterized by having one of the following properties (i-iv).
\textbf{(i) The Type of loss function} that quantifies the distortion is optimizing local neighbourhood~\cite{RoweisLLE2000, Hinton2003} or global graph structure properties~\cite{tenenbaumISOMap, DeepWalk, node2vec}.
\textbf{(ii) The target property} to be preserved is either geodesic (shortest paths)~\cite{Landmarks2009, Landmark2006, Frankl1988} or diffusion distance (heat or random walk process)~\cite{GraphPCA, DeepWalk, node2vec} or another similarity property~\cite{Hinton2003, vanDerMaaten2008, HOPE2016}.
\textbf{(iii) The mapping $\phi$} is a linear~\cite{NMFgraph, HOPE2016} or non-linear dimensionality reduction technique such as SNE~\cite{Hinton2003}, t-SNE~\cite{vanDerMaaten2008}, ISOMAP~\cite{tenenbaumISOMap}, Laplacian eigenmaps~\cite{Belkin2003} or deep learning work DeepWalk~\cite{DeepWalk}, node2vec~\cite{node2vec}, HOPE~\cite{HOPE2016}, GraphSAGE~\cite{GraphSAGE}, NetMF~\cite{MFembeddings} and others~\cite{hamilton2017representation}.
\textbf{(iv) The geometry} of $X$ has zero curvature (Euclidean)~\cite{Linial1995, Frankl1988}, positive curvature~\cite{Hancock2014} (spherical) or negative curvature ~\cite{Kriukov, Cannistraci2017, NeuralHyperbolic} (hyperbolic) or mixed curvatures \cite{Gu2019LearningMR}.

However, for directed graphs the asymmetry of graph shortest path distances $\operatorname{d}_G(v_i,v_j) \neq \operatorname{d}_G(v_j,v_i)$ is violating the symmetry assumption (not a metric function).
This is the reason why only recently this problem was tackled by constructing two independent representations for each node (source and target representations)~\cite{HOPE2016, APP2017, Avishek18}.
In this paper, we propose a single-node embedding for directed graphs to the space of probability distributions, described with the geometry of statistical manifold theory.
Up to this point, different probability distributions were used for different embedding purposes such as: (i) applications to word embedding  \cite{Rudolph16,VilnisGaussWords}, knowledge graphs \cite{GaussKnowledgeGraphs}, attributed graphs \cite{bojchevski2017deep} or
(ii) generalized point embeddings with elliptical distributions \cite{Muzellec18}, but not for low-dimensional embedding of directed graphs, characterized by the theory of statistical manifolds.

The \textbf{main contributions} of this paper are:
(i) We propose a node embedding for directed graphs to the elements of the statistical manifolds~\cite{StatManifold}, to jointly capture asymmetric and infinite distances in a low-dimensional space.
We determine the connection between geometry and gradient-based optimization.
(ii) Furthermore, we develop a sampling-based scalable learning algorithm for large-scale networks.
(iii) Extensive experiments demonstrate that the proposed embedding outperforms several directed network embedding baselines, i.e.\ random walks based~\cite{APP2017} and matrix factorization based~\cite{HOPE2016} methods, as well as the deep learning undirected representative~\cite{DeepWalk} and deep Gaussian embeddings for attributed graphs~\cite{bojchevski2017deep}.

\section{Statistical manifold divergence embedding for directed graphs}
In this section, we analyze the limits of metric embedding.  Then, we continue by providing an intuition of our embedding with a synthetic example before presenting the learning procedure of the embedding method.
Bourgain theorem (1985) \cite{Bourgain1985} states that it is possible to construct an $O(\log(n))$-dimensional embedding ($\phi\colon v \mapsto x_v$) of an undirected graph with $n$ nodes to Euclidean space with $O(\log(n))$ distortion by using random subset projection.
But, on directed graph $G=(V,E)$, the directed shortest paths are not necessarily symmetric $d_{v,u}\neq d_{u,v}$. This does not imply that it is not possible to provide low dimensional metric embedding~\cite{Bourgain1985, Linial1995, Johnson1986, Frankl1988} with a variant of Bourgain theorem. In the appendix \ref{app:limits}, we show that the main problem is not asymmetry, but the existence of infinite directed distances, which is quite common in real directed networks, see Table 1.

\subsection{Learning statistical manifold embedding}

Instead of a conventional network embedding in a metric space, we propose to use the Kullback-Leibler divergence defined on probability distributions to capture asymmetric finite and infinite distances in directed graphs.
Each node is mapped to a corresponding distribution described by some parameters, which we learn during training.
In this paper, we choose the class of exponential power distributions due to analytical and computational merits.
With minor modifications, our method can also be applied to other distribution classes.

\begin{figure}
  \centering
  \includegraphics{toy_example_smaller.pdf}
  \caption{
  Visualization of synthetic example network together with our embedding.
  The graph is embedded into the 2-variate normal distributions, which are represented by a $\sigma$ ellipse (boundary of 1 standard deviation around mean $\boldsymbol{\mu}$).
 The $\sigma$ ellipses of the green nodes are contained in one of the greys nodes, which represents that the distance (measured with divergence in embedded space) between green and grey is small, but in the opposite direction very large.
  }
  \label{img:toy_example}
\end{figure}

\textbf{Intuition.}
In Fig.\ \ref{img:toy_example}, we use a toy example to qualitatively illustrate that our embedding can represent infinite distances without requiring a high dimensional embedding space.
More precisely, the network consists of five groups (color-coded), with five nodes each.
The two top blocks have a connection only to the center group and, similarly, the two bottom groups have only a link from the central group to them.
Our model learns this connectivity pattern by embedding members of these groups in a similar fashion.
In this example, each node $u$ is embedded as a 2-variate normal distribution with mean $\boldsymbol{\mu}_u=(\mu_u^1, \mu_u^2)$ and variances $\boldsymbol{\Sigma}_u = \operatorname{diag}(\sigma_u^1, \sigma_u^2)$, which can be visualized by the $\sigma$-ellipse, the curve which represent one-$\sigma$ distance from their respective means.
The nodes of graphs are embedded by minimizing the difference between the pair-wise graph distances and corresponding Kullback-Leibler divergences between embeddings.
For two distribution $p(x),q(x)$, if $p(x)\gg q(x)$ on an open subset $U$, the Kullback-Leibler divergence $\operatorname{KL}(p(x), q(x))$ is then relatively high.
In other words, if in Figure \ref{img:toy_example} the $\sigma$-ellipse of node $u$ is contained in or very similar to the $\sigma$-ellipse of node $v$, then the embedding represents $\operatorname{d}(u,v) < \infty$.
Using this, we see that the embedding retrieved by our optimization and visualized in Fig.\ \ref{img:toy_example} includes most of the observed infinite distances.

\textbf{Statistical manifold embedding.}
Our embedding space $X$ is the space of k-variate exponential power distributions \cite{Nadarajah2005} (also called generalized error distributions), described by the following probability density function for $\lambda > 0$
\begin{align}
\psi_\lambda(x|\boldsymbol{\Sigma}, \boldsymbol{\mu}) = \frac{\lambda\Gamma(\frac{k}{2})}{2^{1+\frac{k}{\lambda}}\pi^{\frac{k}{2}}\det(\boldsymbol{\Sigma})^{\frac{1}{2}}\Gamma(\frac{k}{\lambda})}\exp\left(-\frac{[(x-\boldsymbol{\mu})^T\boldsymbol{\Sigma}^{-1}(x-\boldsymbol{\mu})]^{\frac{\lambda}{2}}}{2}\right)
\label{eq:generailzed_pdf}
\end{align}
with mean vector $\boldsymbol{\mu} \in \mathbb{R}^k$ and covariance matrix $\boldsymbol{\Sigma} \in \mathbb{R}^{k\times k}$.
$\Gamma(.)$ denotes the Gamma function and $x^T$ denotes the transposed vector of $x$.
Note that $\lambda=2$ results in the multivariate Gaussian distribution and $\lambda=1$ yields the multivariate Laplacian distribution.

As we are interested in non-degenerate distributions, we enforce positive definite co-variance matrices and further restrict ourselves to diagonal matrices, i.e.\ $\boldsymbol{\Sigma}_u=\operatorname{diag}(\sigma_u^1, \dots, \sigma_u^k)$ with $\sigma_u^i\in \mathbb{R}_{+}$.
With the latter, we reduce the degrees of freedom for each node to $2k$ and simplify our optimization by replacing a positive definite constraint on $\boldsymbol{\Sigma}_u$ with the constraints $\sigma_u^i>0$.
A common asymmetric function operating on continuous random variables is the Kullback-Leibler divergence.
The asymmetric distance between nodes $u$ and $v$, denoted by $\operatorname{KL}_{u,v}$, is
\[
\operatorname{KL}_{u,v} = \operatorname{KL}(p_u^\lambda, p_v^\lambda)
= \int p_u^\lambda \log \frac{p_u^\lambda}{p_v^\lambda} \operatorname{dx}
\]
with $p_u^\lambda = \psi_\lambda(x|\boldsymbol{\Sigma}_u, \boldsymbol{\mu}_u)$ and $p_v^\lambda = \psi_\lambda(x|\boldsymbol{\Sigma}_v, \boldsymbol{\mu}_v)$.
We approximate it with importance sampling Monte Carlo estimation.
In particular, $\operatorname{KL}_{u,v}$ can be expressed as the expectation (for $\lambda_*=2\le \lambda$)
\[
\operatorname{KL}_{u,v}
= \mathbb{E}_{x \sim p_u^{\lambda_*}} \left[\frac{p_u^\lambda}{p_u^{\lambda_*}}\log\frac{p_u^\lambda}{p_v^\lambda}\right],
\]
where $p_u^{\lambda_*} =\psi_{\lambda_*}(x|\boldsymbol{\Sigma}_u, \boldsymbol{\mu}_u)$ is easy to sample and a proposal function for $p_{\lambda}(x|\boldsymbol{\Sigma}_u, \boldsymbol{\mu}_u)$.
If a closed expression for $\operatorname{KL}_{u,v}$ is known, its evaluation replaces the importance sampling Monte Carlo estimation, like in the special case of $\lambda=2$. See Appendix \ref{app:importanceSampling} for more details.

Now, in order to learn these statistical manifold embeddings, we can define the loss function over
asymmetric distances $D = (\operatorname{d}_{u,v})_{u,v\in V}$ of a directed graph and $\{ (\boldsymbol{\mu}_u, \boldsymbol{\Sigma}_u) \}_u$ as:
\begin{align}
\label{eq:objective}
    \mathcal{L}( \{ (\boldsymbol{\mu}_u, \boldsymbol{\Sigma}_u) \}_u ) = \sum_{u\ne v}||(1+\tau \operatorname{KL}_{u,v})^{-1}-\operatorname{d}_{u,v}^{-\beta}||_2^2,
\end{align}
where $\tau\in \mathbb{R}_{+}$ is a free (trainable) parameter and $\beta\in \mathbb{R}_{+}$ a fixed value.
This loss function given in the Eq.~(\ref{eq:objective}) is minimized during the learning process so that the $\operatorname{KL}_{u,v}$ based on learned node distribution representations captures the distances $\operatorname{d}_{u,v}$ in the directed graph.
Empirically, we transform the given distances into finite numbers with $\operatorname{d}_{u,v}^{-\beta}\in[0,1]$, for all $u \ne v$ and a $\beta\in \mathbb{R}_{+}$, which can be used to increase the differentiation between large $\operatorname{d}_{u,v}$ values and between finite and infinite distances.
We modify the unbounded Kullback-Leibler divergence in a similar fashion $(1+\tau \operatorname{KL}_{u,v})^{-1} \in[0,1]$, where $\tau\in \mathbb{R}_{+}$ is a free (trainable) parameter and the additional $1$ ensures a value in the same interval $[0,1]$.
We start the optimization from random initial parameters $\{\boldsymbol{\mu}_u, \boldsymbol{\Sigma}_u\}_{u\in V}$, and iteratively minimize the loss function with stochastic gradient descent optimizers, such as Adam~\cite{kingma2014adam}.
For small $k$, which we will consider in the next section, more enhanced initialization strategies, such as using graph plotting algorithms to determine the means and exploiting memberships of strongly connected components for the covariance initialization, probably reduce the number of training epochs.

\textbf{Scalable learning procedure.}
The full objective function Eq.~(\ref{eq:objective}) consists of $|V|(|V|-1)$ terms and only graphs with up to the magnitude of around $10^4$ nodes can be applied.
To extend our method beyond this limit, we propose an approximated solution, where the size of the training data scales linearly in the number of nodes and thus can be applied to large graphs.

This approximation solution is based on a decomposition of the loss function Eq.~(\ref{eq:objective}) into a neighborhood term and a singularity term as:
\begin{align*}
    \mathcal{L}( \{ (\boldsymbol{\mu}_u, \boldsymbol{\Sigma}_u) \}_u ) &=
    \underbrace{\smashoperator[r]{\sum_{u\ne v, \operatorname{d}_{u,v}<\infty}}||(1+\tau \operatorname{KL}_{u,v})^{-1}-\operatorname{d}_{u,v}^{-\beta}||_2^2}_{\text{neighborhood term}} + \underbrace{\smashoperator[r]{\sum_{u\ne v,  \operatorname{d}_{u,v}=\infty}}||(1+\tau \operatorname{KL}_{u,v})^{-1}-\operatorname{d}_{u,v}^{-\beta}||_2^2}_{\text{singularity term}}.
\end{align*}

We efficiently sample from each of these sums for each node a small number of $B$ samples and optimize our model based on the sampled information about closeness and infinite distances.

One straightforward approach to approximate the neighborhood term is to use all direct neighbors.
Yet, the number of samples available via the directed neighbors is limited by $|E|$, which usually corresponds to a small $B$ in sparse real-world examples.
Therefore, we apply for each node a breadth-first-search into both directions until we retrieve $B$ new samples.
In this way, we obtain smaller $\operatorname{d}_{u,v}$ first, and the approximation tends to the original term in the limit $B\to |V|$.

For the singularity term, we use the topological sorting~\cite{Kahn1962} of the strongly connected components.
In a topological sorting of a directed acyclic graph, all edges are from lower indices to higher indices (with respect to the topological sorting).

Two important restrictions are noteworthy.
First, the reverse statement does not hold, i.e.\ not all infinite distances are given via a single topological sorting~\cite{Kahn1962}.
As a consequence, we won't sample uniformly from all infinities, but from a subset of the infinities given by the topological sorting.
Second, the construction of topological sorting is only possible for acyclic graphs, and most directed graphs have cycles.

Since we are only interested in sampling singularities and the graph defined by the strongly connected components of a directed graph is acyclic, we use Tarjan's algorithm~\cite{tarjan1972} to retrieve the strongly connected components and topological sorting of them.
After this preprocessing, we can efficiently generate samples of infinite distance for the nodes.

With these two approximations we can construct two sets $U_{\text{close}}$ and $U_{\infty}$ and the loss function reduces to
\begin{align}
    \Tilde{\mathcal{L}} &=
        \sum_{(u,v)\in U_{\text{close}}}||(1+\tau \operatorname{KL}_{u,v})^{-1}-\operatorname{d}_{u,v}^{-\beta}||_2^2
    &\ + \sum_{(u,v)\in U_{\infty}}||(1+\tau \operatorname{KL}_{u,v})^{-1}-\operatorname{d}_{u,v}^{-\beta}||_2^2. \nonumber
\end{align}

\begin{lemma}\label{lemma:complexity}
Let $G=(V,E)$ be a graph. Then our embedding has $2k|V| + 1$ degrees of freedom and one hyperparameter ($\beta$).
Generating the training samples for the full method, is equivalent to the all pair shortest path problem, which can be solved within $O(|V|^2\log|V| + |V||E|)$ for sparse graphs and evaluating the loss function has time complexity $O(|V|^2)$.
The scalable variant has, for $B\ll|V|$, a time complexity of $O((B+1)|V|+|E|)$ and the loss function has $O(B|V|)$ terms.
\end{lemma}
See Appendix \ref{app:complexity} for proof using Johnson's algorithm~\cite{johnson1977efficient} and the pseudo-code of our methods.

\section{Statistical manifolds and directed graph geometry}
In this section, we show the properties of our representation space and the effects on our learning procedure.

\begin{theorem}
\label{theorem:properties}
Let $\lambda$ be an even number.
Then the distributions with density of Eq. (\ref{eq:generailzed_pdf}) and parametrization $(\sigma^1, \dots \sigma^k, \mu^1, \dots, \mu^k) \mapsto \boldsymbol{X} \sim\psi_\lambda(x|\boldsymbol{\Sigma}=\operatorname{diag}(\sigma^1,\dots,\sigma^k), \boldsymbol{\mu}=(\mu^1, \dots, \mu^k))$ with $\sigma^i > 0$ are a statistical manifolds $\mathcal{S}$ with the following properties:
\begin{enumerate}
    \item For univariate exponential power distribution, the curvature is constant and equal to $-\nicefrac{1}{\lambda}$.
    \item the Fisher information matrix, i.e.\ the Riemannian metric tensor, in this coordinate system at point $\psi_\lambda(x|\boldsymbol{\Sigma}, \boldsymbol{\mu})$ with $\boldsymbol{\Sigma}=\operatorname{diag}(\sigma^1,\dots,\sigma^k)$ and $ \boldsymbol{\mu}=(\mu^1, \dots, \mu^k)$ is given by
    \[
    (g_{ij})_{ij} = \operatorname{diag}\left(\frac{c_1}{(\sigma^1)^2}, \dots, \frac{c_1}{(\sigma^k)^2}, \frac{c_2}{(\sigma^1)^2}, \dots, \frac{c_2}{(\sigma^k)^2}\right),
    \]
    where
    \[
    c_1 = \frac{\Gamma(1-\frac{1}{\lambda})\lambda(\lambda - 1)}{\Gamma(\frac{1}{\lambda})}, \quad c_2 = \lambda.
    \]
    \item the Riemannian distance between $\psi_\lambda(x|\boldsymbol{\Sigma}, \boldsymbol{\mu})$ and $\psi_\lambda(x|\boldsymbol{\Tilde{\Sigma}}, \boldsymbol{\Tilde{\mu}})$ is
    \[
    \operatorname{d}_{F}( \psi_\lambda(x|\boldsymbol{\Sigma}, \boldsymbol{\mu}), \psi_\lambda(x|\boldsymbol{\Tilde{\Sigma}}, \boldsymbol{\Tilde{\mu}})) =
    \sqrt {\lambda \sum\limits_{i = 1}^k {{{\textrm{arcosh}}^2}\left( {1 + \frac{{{{\left( {\frac{{{\mu ^i} - {{\widetilde \mu }^i}}}{{{c_3}}}} \right)}^2} + {{\left( {{\sigma ^i} - {{\widetilde \sigma }^i}} \right)}^2}}}{{2{\sigma ^i}{{\widetilde \sigma }^i}}}} \right)} }
    \]
    with $c_3=\sqrt {\frac{{\Gamma \left( {\frac{1}{\lambda }} \right)}}{{\left( {\lambda  - 1} \right)\Gamma \left( {1 - \frac{1}{\lambda }} \right)}}}$.
\end{enumerate}
\end{theorem}
See the Appendix \ref{app:theorem1} for more details and proofs.

The first observation of Theorem \ref{theorem:properties} is that the geometry of the statistical manifold has constant negative curvature (see \cite{Amari1985, Skovgaard1984, yuan2019geometric} for detailed derivation).
Negative curvature also comes as a natural model for power-law degree distributions in complex networks~\cite{Kriukov, HyberbolicPhysRevE}.
In Fig.\ \ref{img:hyperboloid} of Appendix \ref{app:issomerty} we exemplify the negative curvature of our embedding for the graph representations of Fig.\ \ref{img:toy_example} by making the isometric mapping (preserving distances) to the hyperbolic space. See the Appendix \ref{app:issomerty} for the derivation of the used isometry.
Since our representation space is not flat, the Riemannian metric is different from the Euclidean.
Therefore, we need to adjust the Euclidean gradient $\nabla L$ of one of our objective functions $L\in \{\mathcal{L}, \Tilde{\mathcal{L}}\}$ with respect to the metric tensor~\cite{Amari1998}.
The steepest descent direction is given by $\Tilde{\nabla} L = G^{-1}\nabla L$, where $G^{-1}$ is the inverse of the metric tensor $G$ of $\mathcal{S}^n$ evaluated at the specific point.
In other words, starting from the same representations and performing one step into the direction of $\Tilde{\nabla}L$ will always result in lower values of the loss function $L$ than going into the direction of $\nabla L$.
In our experiments, we report the results using $\Tilde{\nabla}L$.
Further discussions and details can be found in Appendix \ref{app:correction}.

With the last property of our embedding space, the non-Euclidean Riemannian distance, is important for embedding based applications, like clustering.
Note, that the value of $c_3$ is approximately close to the value 1.0 for different values of $\lambda$. It implies that after the embedding is done with one family of distributions, we can see that the pair-wise Riemannian distance of embedded points for different representations ($\lambda$) is scaling only by the multiplicative factor $\sqrt{\lambda}$ (see Appendix \ref{app:theorem1} for more details).
Also note another useful connection, the Hessian of KL divergence is the Fisher information metric $g_{i,j}$.
When the Fisher distance is small, we have
$\operatorname{KL}\left( p_u^\lambda\parallel p_v^\lambda \right) \approx \frac{1}{2}{\operatorname{d}_F}{\left( p_v^\lambda,p_u^\lambda \right)^2} \leqslant {\operatorname{d}_F}\left( p_v^\lambda,p_u^\lambda \right)$.
Furthermore, the Fisher information metric is unique (up to scaling) and is the only Riemannian metric on the statistical manifolds~\cite{Cencos,InfoGeometr}. Due to this fact and its connection with KL divergence, this is the reason why we have not used other divergence functions.

\section{Experiments}
\subsection{Datasets}
From the Koblenz Network Collection~\cite{kunegis2013konect} we retrieved three datasets of different sizes and connectivity. See Table \ref{tab:graph_properties} for an overview.

\textbf{Political blogs.}
The small dataset is compiled during the 2004 US election~\cite{adamic2005political}.
In addition, we evaluate two larger networks with different proportions of reachability between their nodes.

\textbf{Cora.}~\cite{cora} consists of citations between computer science publications and was used as an example in baseline APP~\cite{APP2017} and HOPE~\cite{HOPE2016} as well.

\textbf{Publication network.}
With a higher density but lower reciprocity, our largest example is the {publication network} given by arXiv's High Energy Physics Theory (hep-th) section~\cite{leskovec2007graph}.
\begin{table}
  \caption{\small{Properties of datasets}}
  \label{tab:graph_properties}
  \centering
  \begin{tabular}{lrrcr}
    \toprule
    Name     & $|V|$     & $|E|$  & $|\{d_{u,v}:d_{u,v}=\infty\}|/|V|^2$ & Reciprocity \\
    \midrule
    Synthetic example & 25  & 30 & 0.48 & 34.3\% \\
    Political blogs & 1224 & 19,025 & 0.34 & 24.3\% \\
    Cora & 23,166 & 91,500 & 0.83 & 5.1\% \\
    arXiv hep-th & 27,770 & 352,807 & 0.71 & 0.3\%\\
    \bottomrule
  \end{tabular}
\end{table}
\subsection{Baselines}
\textbf{APP} is the asymmetric proximity preserving graph embedding method~\cite{APP2017} based on the skip-gram model, which is used by many other methods like Node2Vec and DeepWalk.
In contrast to the symmetric counterparts, APP explicitly splits the representation into a source vector and a target vector, which are updated in a direction-aware manner during the training with random walks with reset.
Their method implicitly preserves the rooted PageRank score for any two vertices.

\textbf{HOPE} stands for High-Order Proximity preserved Embedding~\cite{HOPE2016}.
This method uses a generalization of the singular value decomposition to efficiently retrieve low-rank approximation of proximity measures like Katz Index or rooted PageRank.

\textbf{DeepWalk} is the deep learning representative of the undirected graph embedding methods.
It does not differentiate between source and target and retrieves a single representation $s_u \in \mathbb{R}^K$ for each node.
Like APP, DeepWalk uses the skip-gram model, trains the representation with random walks, and evaluates by cosine similarity between two node representations.

\textbf{Graph2Gauss} trains a neural network to output mean and variance of Gaussians in a way such that a ranking loss is minimized~\cite{bojchevski2018deep}.
Their loss function concentrates on preserving the 1-hop and 2-hop neighborhood, but not the global graph structure ($\operatorname{d}_{u,v}>2$).
The method was designed for graphs with additional node feature data, and in the absence of such information, one-hot vector encoding should be used instead.

\subsection{Set-up}
In this paper, we focus on directed network embedding, which preserves global properties of directed graphs.
Considering this, our emphasis is different from the conventional evaluation tasks of network embedding,
e.g. the link prediction task only evaluates the differentiation between $\operatorname{d}(u,v)=1$ and $\operatorname{d}(u,v)>1$, which respectively correspond to the case of link existence and not.

\textbf{Evaluation metrics.}
Three evaluation metrics are used on the pairs of inverse true distances and approximated values derived by learned embedding from baselines and our methods.
We use the \textit{Pearson correlation coefficient} $\rho$ for checking a linear dependency~\cite{shalev2014understanding} and \textit{Spearman's rank correlation coefficient} $r$ for evaluating the monotonic relationship.
In addition to these statistical measures, we propose to use an information-theoretic non-linear evaluation metric, i.e.\ \textit{mutual information} (MI).
We choose the non-parametric k-nearest-neighbor based MI estimator, LNC~\citep{gao2015efficient}.
It is recently developed to overcome local non-uniformity and can capture relationships with limited data.
We briefly describe the LNC MI here as: \linebreak
$\hat{I} = \frac{1}{N}\sum_{n=1}^{} \log \frac{\hat{p}(x, y)}{\hat{p}(x) \cdot \hat{p}(y)} - \frac{1}{N}\sum_{n=1}\log\frac{\bar{V}(n)}{V(n)}$
, where $N$ is the total number of data instances, the second term is a correction term handling non-uniform region $V(n)$ surrounding point $n$, and $\hat{p}(.)$ represents the kNN density estimator.

\textbf{Hyper-parameters.}
With the nonlinear interactions in our embedding, our method needs only a small number of dimensions, and for the presented results we used an embedding into a 2-variate normal distribution, i.e.\ $k=2$ and the number of free parameters for each node is $4$.
The initial means $\boldsymbol{\mu}_u$ and co-variances $\boldsymbol{\Sigma}_u$ are randomly initialized.

For the random walk based methods APP and DeepWalk, we unified both default settings with 20 random walks for each node and length 100.
The embedding dimension was set to $K=4$, where we allowed APP to use two $4$ dimensional vectors.
In the same fashion, we set the embedding dimension for HOPE to $4$, resulting in two $|V|\times 4$ matrices.
All other parameters we left at the default value.
For our proposed embedding, we evaluate both the exact and approximate version.
For the approximate one, we report the results based on $B=10$ and $B=100$ samples for each node.

We executed the optimization with $\beta \in \left\{\frac{1}{4}, \frac{1}{3}, \frac{1}{2}, 1 \right\}$ and consistently retrieved the best results for $\beta=\frac{1}{2}$.
For our method, we selected from the runs the point with the highest Pearson correlation between $\operatorname{d}_{u,v}^{-1}$ and $(1+\tau \operatorname{KL}_{u,v})^{-1}$, which usually coincided with the minimal observed objective function value, like in Figure \ref{img:toy_example}.
In the experiments, we applied Adam optimizer with learning rates in $\{.001, .01, .1\}$ and retrieved the reported results with the $.1$ for political blogs and $.001$ for the others.

HOPE was executed with GNU Octave version 4.4.1, and the other methods were executed in Python 3.6.7 and Tensorflow on a server with 258 GB RAM and a NVIDIA Titan Xp GPU.

\subsection{Results}
First, we compared the performance of the embedding using different $\lambda$, i.e.\ different classes of distributions.
From the results in Appendix Table \ref{tab:generalized_results}, we observe no significant performance improvement.
So we fixed $\lambda=2$ for the other experiments to have the performance gains of the analytical known KL divergence.

Table \ref{tab:results} shows the results of evaluation metrics on different datasets.
$\text{KL}(\cdot)$ and $\text{KL}(\text{full})$ refer to the approximate and exact version of our proposed embedding.
We report the mean and standard deviation of MI by $40$ bootstrap samples.
For all metrics, the higher the value, the better the performance.

The results support this intuition that our method retrieves the highest values in all cases and significantly outperforms baselines.
In addition, our approximated variant using only 10 neighbors and 10 infinities for each node already achieves higher values than baselines.
Increasing the number of samples to 100, further enhances the performance of the approximate version, which even embeds the large network in a similar quality as our full method.
A visualization of the results is given in Figure \ref{img:complete_overview}.
Further details are in the Appendix \ref{app:experiments_details}.
Additional experiments such as dimensionality experiment, graph reconstruction, and evaluation on undirected graph can be found in Appendix~\ref{sec:further_experiments}.

\begin{figure}
  \centering
  \includegraphics{complete_with_labels.pdf}
  \caption{Visualization of approximated values w.r.t. inverse true distance $(\operatorname{d}_{u,v}^{-1})_{u,v}$ with boxplots, representing the means, first and third quartiles as well as $1.5$ interquartile range. Violin plots indicate the kernel densities.
  Columns respectively correspond to dataset political blogs (first column), Cora (second column), and arXiv hep-th (last column).
  Rows from top to bottom represent APP, HOPE, DeepWalk, Graph2Gauss, and our KL method.
  Ideally, as the ground-truth value increases, approximated values are expected to follow this trend as well.
The baseline methods show only a weak separation between the closest distances and all other.
For \textbf{our embedding method} (last row) a monotonic increasing relation is visible, which
was reflected in the highest mutual information, Pearson and Spearman's correlation coefficient in Table \ref{tab:results}.
  }
  \label{img:complete_overview}
\end{figure}

\begin{table}[htbp]
  \caption{Results including Pearson correlation coefficient $\rho$, Spearman's rank correlation coefficient $r$, and mutual information (MI).
  The p-value for $\rho$ and $r$ are in all cases below $10^{-8}$.
  For our method, we include the results with 10 samples, 100 samples for each node, and using the full distance matrix.
  }
  \label{tab:results}
  \centering
  \small
 \begin{tabular}{@{}lllllllll@{}}\toprule
      Network& & APP & HOPE & DeepWalk & Graph2Gauss & KL (10) & KL (100) & KL (full) \\\midrule
      Political  &  $\rho$ & $.16$ & $.45$ & $.25$ & $-.17$ & $.73$ & $.77$ & $\mathbf{.88}$ \\
      Blogs & $r$ & $.29$ & $.45$ & $.24$ & $-.33$ & $.71$ & $.72$ & $\mathbf{.89}$ \\
      & avg. MI & $.15$ & $.65$ &  $.12$ & $.09$ & $.47$ & $.59$ & $\mathbf{.85}$ \\
      & std. MI & $.006$ & $.007$ &  $.005$ & $.005$ & $.005$ & $.005$ & $.006$ \\
      \bottomrule
      Cora & $\rho$ & $.10$ & $.17$ & $.07$ & $-.004$ & $.53$ & $.66$ & $\mathbf{.77}$\\
       & $r$ & $.01$ & $.41$ & $.02$ & $.013$ & $.56$ & $.62$ & $\mathbf{.65}$ \\
      & avg. MI  & $.018$ & $.13$ & $.013$ & $.01$ & $.23$ & $.35$ & $\mathbf{.43}$\\
      & std. MI & $002$ & $.005$ & $.004$ & $.003$ & $.005$ & $.006$ & $.007$\\
      \bottomrule
      arXiv  & $\rho$ & $.01$ & $.20$ & $.08$ & $-.05$ & $.53$ & $.62$ & $\mathbf{.68}$\\
        hep-th & $r$ & $.12$ & $.28$ & $.04$ & $-.06$ &$.59$ & $.68$ & $\mathbf{.72}$ \\
        & avg. MI & $.033$ & $.15$ & $.014$ & $.01$ & $.25$ & $\mathbf{.37}$ & $.36$\\
        & std. MI & $.003$ & $.004$ & $.003$ & $.003$ & $.005$ & $.005$ & $.005$\\
      \bottomrule
      \end{tabular}
\end{table}

\section{Conclusion and discussion}
Although the techniques for graph embedding are quite mature~\cite{hamilton2017representation,MFembeddings,NeuralHyperbolic, DeepWalk, node2vec}, there still exist obstacles for using them for directed graphs. Obstacle arises from the asymmetric property of graph geodesics and a large ratio of pairs with infinite distances.
Motivated by this, we propose a mapping of nodes to the elements of the statistical manifolds~\cite{StatManifold} by minimizing the divergence function between embedded points a graph geodesics. This allows a single representation that allows elegant geometrical encoding of infinite and finite distances in low-dimensional statistical manifolds by using the divergence function. One can encode an arbitrary number of infinities into the low-dimensional space, as this embedding does not need to push point on infinite geodesic distance on a manifold.
Furthermore, this embedding allows the use of analytical tools from statistics and differential geometry, e.g. connection of curvature and natural gradient~\cite{Amari1998, efron2018, Skovgaard1984, Burbea1982}. In contrast to the previous work, we have characterized the geometrical structure of the underlying space to which nodes are being embedded into, and its connection to the learning via curvature corrected gradient. Furthermore, we have proposed an efficient divergence estimation method via importance sampling method.
Theoretical understanding of the geometrical structures for directed graph embeddings opened many interesting theoretical and practical questions. This is the reason why we had to restrict the scope of this work only to unsupervised setting and leave link prediction and node classification task for future work.

\subsubsection*{Acknowledgments}
The work of N.A.F. has been partially supported by the European Community's H2020 Program under the scheme 'INFRAIA-1-2014-2015: Research Infrastructures', grant agreement 654024 'SoBigData: Social Mining and Big Data Ecosystem' and the scheme 'INFRAIA-01-2018-2019: Research and Innovation action', grant agreement 871042 'SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics'.

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Meet the young textile designer in Shipley looking make a mark on the industry
Hundreds of people queuing outside Yorkshire nightclub during coronavirus pandemic blasted by MP
An MP has criticised the people queuing to get into a West Yorkshire nightclub just hours after the government ordered pubs and clubs to close.
By Rebecca Marano
Saturday, 21st March 2020, 3:15 pm
People queuing to get into The Acapulco Club on Friday during the coronavirus crisis. Photo taken by Rebecca Caswell.
A photo shared on social media, which was taken at 9.25pm on Friday, shows huge queues of young people waiting to get The Acapulco Club in Halifax.
Earlier on Friday, Prime Minister Boris Johnson had ordered pubs, clubs, bars, cafes and restaurants to close completely from midnight.Mr Johnson acknowledged that people may be tempted to go out before the measures were introduced - but said "please don't".
Sharing the photograph on Twitter, Holly Lynch, the Labour MP for Halifax, expressed her disappointment at the people queuing.
She said: "This is unbelievably disappointing. I will be speaking to licensing colleagues at Calderdale Council immediately.
"For those queuing you are not just putting yourselves at risk but anyone you might later come into contact with, parents, grandparents, anyone who might be vulnerable."
Staff at the family-run nightclub Acapulco had announced on Thursday that it would be the last weekend that the club was open - before measures were announced to enforce closures.
In the statement, they said the move was to "save lives" and that people's health was "more important than profit."
Simon Jackson, owner of The Acapulco Club, told the Yorkshire Post today that the club has closed and will remain closed.
He added that the staff had put measures in place to ensure people's safety on Friday night.
Mr Jackson said: "Firstly, we want to tell people that the club is definitely not open today.
"As a company, we actually decided last week that this weekend would be our last - this decision was made before the prime minister gave any sort of indication that he wanted pubs and clubs to close.
"We decided to close because we believed it was the safest thing to do for our staff, their families and the town of Halifax. We made this decision despite many other pubs and clubs refusing to close.
"However, we are a very busy, family-owned club and employ around 70 staff. We didn't want to close suddenly and leave them without a job - we wanted to pay the salaries as long as we could. This was before the chancellor announced that there would be financial help.
"We thought it was fair to try to give the staff a little bit of notice so they can get things in order because the reality is that this weekend could be their last wage for months.
"We announced to the public that it would be our last weekend open and urged people to be safe and not to venture out.
"The government's announcement came late on Friday when staff were already expecting to work and many people would already be on their way to town.
"If it had of been earlier then we would have closed immediately but it was too last minute.
"We worked with the police and said we would discourage people from staying late and play long section of slow music to try make crowds disperse in an orderly manner. The police were happy with the measures we took.
"We also took precautions like extra hand sanitiser inside the club and taking people's temperatures before they were allowed in. Anyone who was sick was not allowed in.
Mr Jackson added: "Like most other clubs and pubs across the country, we stayed open on Friday and closed up for good when the night ended. We will remain closed for the foreseeable future.
"We will not reopen until the government says it is safe to do so.
"We think Boris Johnson and the government are doing an amazing job in these difficult and unbelievable circumstances.
"We urge all of our wonderful staff and customers to stay safe and try to look after each other, especially the older or weaker people within our community.
"Only buy the essentials you need and give those more vulnerable the opportunity to shop for what they need, when they need it.
"Hopefully in the not too distant future, when things are back to normal, we will be seeing your smiling faces once again."


The minerals illustrated on this site are presented for reference purposes only. The owner of this site did not collect the minerals and collectors should not assume access is permitted to the property based on inclusion in this museum. Please contact the property owner to obtain legal permission before entering private property.


I have the best dad on the planet. You might have another candidate in mind for that award. Please allow me to present my dad's qualifications.
My dad is a handsome guy with dark, almost black, curly hair, dark brown eyes and a year-round tan that betrays his Italian heritage. I always think of Dad's hands as being perpetually dirty. He does wash them, but some dirt takes a long time to come out and my dad's hands have had oil repeatedly ground into every groove and swirl. Dad loves ice cream, laughing, telling stories, cars, country life, babies and his wife, but the thing that really sets him apart is his ability to fix just about anything.
I suppose living in a farm community gave him ample opportunity to tinker with things. He worked on tractors, lawn mowers and cars throughout his teen years. When he joined the Navy, it only made sense that he would continue using his mechanical skills. However, he took the opportunity to play with engines that were just a little bigger. During his time in the Navy, Dad worked on the A-4 Skyhawk, the A-7 Corsair, the F-4 Phantom, the A-6 Intruder, the F-14 Tomcat, and the P-3 Orion. Those are the ones Hubby and I can remember, but there are probably more.
Dad enjoyed working with jet engines, but his favorite project is his 1964 Triumph TR4. I don't know when he bought it, but the Triumph has been around as far back as I can remember. I remember riding in the back seat when I was just a little girl and Dad would take it around the block. It was a sweet little ride. Dad wants to get it running again and put in a Ford V-6 engine for more power.
In his free time, Dad used to comb the Trading Post for good fix-up deals and cruise neighborhoods looking for lawnmowers, garden tractors and the like that people put to the curb. He would take these dead machines home, make repairs that were simple for him and sell them. He even had a rather popular "Man's Yard Sale" every once in a while.
Even with the occasional yard sale, Dad once had a really impressive collection of very important stuff that would have thrilled American Pickers. He had air tanks, alternators, axles, batteries, belts, circuit boards, dipsticks, fuses, gaskets, hinges, hoses, knobs, latches, pedals, sensors, sparkplugs, steering wheels, switches, valves, wires, and tools of all kinds. Unfortunately, a fire erupted in one of his garages and Dad had to call for help. I imagine seeing the NASA fire department respond to a garage fire at my dad's house made the neighbors a little nervous. He does live right outside of Langley Air Force base, but perhaps their concern was valid given the gasoline, paint thinner, oil, fuel, lubricant and other flammable materials he had stashed away in there. That was one hot fire. Thankfully not a soul was singed, but most of his best goodies were lost.
Dad's Mr. Fix-It abilities are not limited to engine repairs. He has quite the green thumb. Years ago he worked at a nursery. He would pick through the throw-away pile for plants he liked and bring them home. In a short time those plants would flourish under Dad's care.
Even babies are subject to Dad's skills. The first time Dad met Polar Bear, the little guy was fussy. Dad picked him up and fixed that problem. Within five minutes, Polar Bear filled his diaper and fell asleep. I can only remember one or two times that my dad couldn't get a baby to fall asleep in his arms.
There is nothing this man can't fix, build, or make better and the most impressive repair Dad ever performed was on our relationship. My parents' divorce was rough and I was an angry teenager stuck in the middle. Things got nasty and I didn't talk to my dad for about 5 years.
While I was pregnant with Polar Bear, Dad asked my uncle to forward a letter to me. In that letter, he did the one thing I never expected him to do. He apologized for everything that had gone wrong in our relationship and asked for another chance. I gave him that chance. You are getting the short sweet version, but it wasn't a quick or easy fix. We had to rebuild entirely. Dad took a risk on something that had been left on the curb and he fixed it up. I think he would agree with me that the rebuild was well worth the effort.
For the past four years, Dad has been fighting Acute Myelogenous Leukemia (AML). Because leukemia is cancer that grows in the bone marrow, the only cure is to eradicate and replace the bone marrow. Dad isn't an easy match so none of his family or the registered bone marrow donors would work for him. Thankfully, there is a procedure that uses babies' cord blood to replace the bone marrow. Dad went through such a procedure two years ago. Unfortunately, one of the cords came from a child who had Myelodysplastic Syndrome, which turns into AML. Doctors do check the cords, but they cannot check for everything. For my father to receive a cord blood transplant that gave him a new form of Acute Myelogenous Leukemia is a one-in-a-million thing. Nobody saw this coming. It still feels surreal. This spring the MDS finally turned into leukemia. It was quickly put into remission and Dad was ready for the transplant when they realized he had fungal pneumonia. After weeks of treatment, Dad had half of one lung removed to get rid of the infection. We hoped he would have time to recover fully before the chemo had to start again, but last week we found out that the leukemia has already returned.
Now we take a deep breath and start again. Dad will have chemo to get the leukemia into remission. Then he will need to be strong enough to go through the harsher chemo to totally eradicate his bone marrow. They already have two cord bloods ready and waiting for him.
This leukemia is one thing my dad can't fix, but it is something he can fight. He has chosen to keep on fighting and my family will support this fight as much as we can. We are abundantly grateful for those friends who have encouraged our family through prayers, notes and practical things like watching my children so I can go and be with my dad. Every moment I can spend with him is a gift.
Because Dad hauled our relationship off the curb, we have had 12 years to really get to know each other. Because of Dad's leukemia, we have had conversations that have cleared out any remaining hurt or concern. Not everybody can say that their relationship with their dad is solid, but then, not everybody has a dad who can repair just about anything from jet engines to relationships. He really is the best dad on the planet.


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Utah shocks UCLA 93-92 with 3-point buzzer beater
By Jarred Davis, Contributing Writer
Utah guard Parker Van Dyke (5) makes a winning three-point basket over UCLA guard Chris Smith (5) and guard David Singleton (34) as time expires during the second half of an NCAA college basketball game Saturday, Feb. 9, 2019, in Los Angeles. (AP Photo/Marcio Jose Sanchez)
The UCLA Bruins held a 22-point lead and were in cruise control, clinging to snap a three-game losing streak.
In an instance, the unthinkable happen and Pauley Pavilion was at a loss for words when watching what had just taken place. Utah's Parker Van Dyke buried a heavily contested NBA range 3-pointer at the buzzer to give the Utes it's only lead of the game as the time expired.
Van Dykes fifth three-point basket of the game capped a 23-9 run in the final 2:45 of play and Utah went on to steal a 93-92 victory over UCLA on Saturday.
The Bruins led 69-47 with 12:10 to play before the wheels fell off completely, allowing the Utes to close the game on a 48-25 run.
"Maybe got a little tentative the last six, seven minutes of the game and last four, five minutes of the game we just couldn't get a stop," Bruins interim coach Murry Bartow said. "They made some incredible plays. We turned it over and missed a bunch of free throws."
"It was 10, then eight. Before you could blink, they make a great, great shot to end the game."
Timmy Allen scored 22 points, Sedrick Barefield added 19 points, and Riley Battin and Van Dyke had 18 and 15, respectively for the Utes (13-10, 7-4 Pac-12).
"It's been a wild season," UCLA guard Jaylen Hands said on the loss. "It hurt a lot."
Hands scored a career-high 27 points for the Bruins (12-12, 5-6), including going a perfect 5 of 5 from the field in the first half with 12 points and five assists.
Kris Wilkes had 17 points, Jalen Hill chipped in with 12, while Cody Riley and Jules Bernard both added 11 points each.
UCLA freshman center Moses Brown was not in the starting lineup for the first time in 23 games this season due to being late to morning shootaround.
"I'm not going to put up with it," Bartow said, only playing Brown 5.3 seconds on the final possession of the game. "He's gotta learn to be on time."
UCLA shot an impressive 58.5 percent from the field but missed eight free throws overall (22-30), especially in pivotal moments.
Seven of the Bruins eight missed free throws came in the final 6:56 of play.
Unfortunately, free throw wows have been nothing new for UCLA, as they came into the game being ranked No.343 in the nation at the line.
"We missed a few throws, I know I missed two," added Wilkes.
Leading 89-85 after a Riley Dunk, the Utes responded with a huge 3-point basket by Both Gach with eight seconds left to get Utah within one, 89-88.
UCLA's David Singleton made two free throws, followed by Barefield matching Singleton at the line, allowing the Bruins to hold a 91-90 lead with only five seconds remaining.
On the next possession, Singleton split a pair of free throws and gave the Utes the one chance they needed.
Barefield received the inbounds pass and dribbled straight into two UCLA defenders passing half court. However, trailing was Van Dyke, who caught the pass and got off an incredibly difficult degree of shot over the outstretched arms of Singleton and UCLA forward Chris Smith.
13,800 fans witnessed the shot they couldn't believe that went in, as the ball ripped the bottom of the net, giving Utah the emphatical 93-92 victory over the Bruins.
UCLA looks to get back to winning when they travel to face the California Golden Bears (5-18, 0-11), who are the sole Pac-12 team without a victory in conference play.
Tipoff from Haas Pavilion is Wednesday at 7:00 p.m.
Categories: Basketball | Sports
Tags: California Golden Bears | Cody Riley | Jalen Hill | Jules Bernard | Kris Wilkes | Moses Brown | Pauley Pavilion | UCLA
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Most of these are links to other sites, due to my concerns about copyright infringement. Use the BACK button on your browser to return to this page after you've viewed a link.
Stephen Bloch's archive of tent documentation/illustrations--a must see!


We provide high end costumed characters as entertainment to special events (mostly kid events). I work as a professional costume designer. The costumes are not your typical Halloween costume but beautiful and believable characters made with gorgeous fabrics and lots of detail. The entertainer in the costume is also a musical theater performer. The characters attend the event (usually as a couple, Princess and Prince) and put on a staged skit or musical number. We have all kinds of people faced characters (no animals). The most common are Princess (Aura and Cinderella) and super hero (Batman, Superman). Other characters are: Clowns, Myley Cyrus/Hanna Montana, High School Musical, Jonas Brothers. The actors will be impersonating these icons while singing and teaching the audience a song/dance number.
We need a logo that is both feminie and masculine. You should know by looking at the design that we represent iconic "impersonated" characters that sing and dance. It should be young, fun, musical, creative and professional.
An idea I had is to make one side of the buisness card for "Super Hero" parities (with both boy and girl images) and on the other side "Fairytail or Pop Singer" parties (also with girl and boy images). The logo would be used on both sides of the buisness card.
The logo will be used for buisness cards, post cards, flyers web design ect.
The target audience is high income families who want to put on an entertaining party that the entire town will be talking about.
Beauty and the Hero, is a company that stands above the average child entertainer. We provide a high-end service that creates a fun and enjoyable expieriene for all ages.
Super Hero with just the bare chest (no head, so no iea what super hero it is)...Maybe pulling the shirt open? A hand breaking thru the "O" in Hero.
Beauty written in script, Hero in block letters.
A wand that starts out with small stars that falls into large block stars.
I don't want copied Disney images but it must be something familar. The quality of the logo should not be childish but should evoke excitement in the parent as well as the child.


There are so many ways you can get involved with Macquarie University and support programs. These programs can help shape the lives of future students, support vital research programs, important community projects and contribute to worldwide solutions to global change.
Interested in holding a community fundraising event?
Download the community fundraising guidelines which contains important information to help you on your way to a successful event.
There are so many ways you can fundraise to support Macquarie University – take on a fitness challenge; hold an afternoon tea or dinner with friends; host a trivia night – the opportunities are endless. Your support will make a difference to the lives of future generations.
When you lose someone close to you, a donation to Macquarie University will honour your loved one and help their memory live on.
Your gift can be used to support the area of greatest need, or can be to a specific fund or program.
Shine a light on your idea and give it a kick start to turn that idea into reality.
Each year, the fire fighting community unite in the fight against MND by climbing the 98 floors of the Sydney Tower Eye in full fire fighting gear.


You're hungry, looking for the perfect place to jumpstart your career; we're looking for talent with great skills. As a visual design intern you'll have the opportunity to hone your talent to perfection, working with the best people in the business.
We do seriously good work without taking ourselves to seriously.
• Hi problem, meet solved!
All our work, everything we do, revolves around the customer journey. We're not going to explain the entire concept here, but basically: the end user is the overlord. Just keep that in mind. If you join us, you will be an end-user-centric-journey-model-expert in no time.
Do you want to work for top (inter)national brands? Curious about what you can accomplish in six months? So are we. Ready to dive in? Send your motivation and (online) portfolio!


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Categories: Reflections
This is the dawning of the age of praxis?
As part of the book writing (and the Cognitive Edge business plan) several of us have been looking at models of change over time. I have always been an admirer of Charles Handy and had the privilege of speaking on the same platform in Dublin. In his classic book The Empty Rain Coat he elaborated his concept of the sigmoid curve. These can be used to describe any life cycle. The basic idea is that all new ideas start slowly, often staggering through the adoption phase on raw belief alone, then they rise to a pinnacle of success followed by decline. The secret of success is to adopt the next curve early (the yellow areas on the diagram below); not too early and not too late. The paradox is that you should make changes while the old model is still working, and commit the resources to take it though the early stage. The longer you leave it the hard it comes and if you wait until you are past the yellow zone the cost is massive and many organisations at this point fail.
I've attempted to apply sigmoid curves to the wider history of management both for understanding and to give a perspective on where we go next, and an optimistic take on the current recession. A rough summary below which will be greatly elaborated in the preface to the book. All and any crticism (constructive or otherwise) would be appreciated, either as comments or by email
As you can see I have elaborated three signmoid curves. The first is the period of scientific management from Taylor through until the 1980s where we see the adoption of process based approaches. We move from the functional structure of the traditional manufacturing environment with its separation of marketing, from sales, from production to the process orientation triggered by Hammer and Champy's Re-engineering the Corporation . With that we see the growth of management consultants, ERP systems and a radical transformation of companies. That period includes knowledge management which adopted the same codification strategy that was implicit in process management, and utilised similar consultancy methods and approaches to the use of technology. Interestingly the old functional form was maintained, but matrix organisations emerged and there was a general downsizing of middle management.
Companies late to adopt the change suffered, and the dominant software of the period was probably SAP. Now while we may think of process as common place and even at times carried to excess in approaches such as Sick Stigma back then it was a revolutionary new idea. IBM rejected a German business plan to create the first ERP system (SAP). It took a recession and the need to reduce costs to trigger a move to process. In general the move was late and the cost was high, and that cost rose as time went on for more conservative organisations.
While those two periods were different, at their heart they assumed an ordered system and the discoverable existence of best practice. The next sigmoid is I think a phase shift (I am trying to avoid the paradigm word). It involves the move to an understanding of complex system, with their inherent un-causality, their nature requiring an approach based on the three basic characteristics of distributed cognition, fine granularity objects and disintermediation. I have previously elaborated those here and will expand further on their application in that series over the next few months. This shift has been characterised by the phenomena of social computing, but until recently that has not been a corporate tool, more a private one. Now we have a recession, we have an immediate a chronic need to rethink our organisations and markets and that needs a new way of perceiving the world.
For me that has always been the natural science based approach to sense-making, incorporating complex adaptive systems, narrative and the like into a vody of method and tools that allow us (as process did before) to do more with less. That new paradigm is emerging and in case you hadn't realised it Cognitive Edge and SenseMaker™ were a rejected IBM business plan ….
One of the major changes here is the shift from attempting to replicate what has worked for others with the inevitable confusion of correlation with causation and the failure to appreciate the criticality of context, to an approach in which sound theory is applied to practice. When we understand the principles of complexity, such as agent interaction, attractors etc. then we can apply that theory to a novel situation with some confidence of a good outcome. It will not replicate past success, but will be appropriate to the current context. That used to be called praxis and is I think at the heart of this new age, and a key and controversial skill so its adoption will not be easy. Back in the seventies we used to joke that praxis makes perfect. In an age of uncertainty that is not longer a joke, its a key opportunity.
The Strategic Mind & "awkward passion"
The Power of Social Networks


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May Day Festival of Solidarity, Barnsley
Sunday, May 1, 2016 - 12:00 to 22:00
Janine will be the main stage compere at the MAY DAY FESTIVAL OF SOLIDARITY between 12pm and 10pm on SUNDAY MAY 1ST 2016 at BARNSLEY ROCK & BLUES VENUE, S75 2BA.
The festival will be a celebration of International Worker's Day, bringing together Socialist voices from up and down the land.
Musicians and poets will join speakers from unions and campaigning groups all coming together in the spirit of unity and defiance.
The two-stage line-up includes: Grace Petrie, The Wakes, The Black Lamps, Phil 'Swill' Odgers, Lizzie Nunnery & Vidar Norheim, The Hurriers, Steve White & The Protest Family, Headsticks, Alun Parry, Sheffield Socialist Choir, Quiet Loner, Serious Sam Barrett and Joe Solo with more to be added.
Tickets are £10 with a donation from each going to Orgreave Truth and Justice Campaign. They are available here.
Please 'Like' and 'Share' and 'Retweet' us. We are:
TWITTER: @ComradeRockUK
Justice For The 33
An Ode to Tube Cleaning
West Coast Franchising Chaos
ETF Women's Committee, March 2014 - Campaigns and Other Business
ETF Women's Committee, March 2014: section and country reports


As most people are aware, the relationship between the settlers and the original inhabitants of my adopted country has often been, shall we say, far from ideal (some would say that little has changed…). When the British claimed the continent for the crown, they declared it Terra Nullius (Latin for 'land belonging to no-one'), conveniently ignoring the Aborignal tribes who had been living there for tens of thousands of years. In many cases, this clash of cultures resulted in imprisonment, de facto slavery and (in at least one case) virtual extermination.
Kim Scott's novel That Deadman Dance attempts to show that this (like so many other things) happened slightly differently over in the West. Set on the southern coast of Western Australia in the 1830s/1840s, this year's Miles Franklin Award winner depicts an intriguing slice of Australian history, a period when colonists and natives coexisted in relative harmony. Scott has Aboriginal ancestry, and his story incorporates members of his family, from the Noongar people, and actual events of the period, using history as the base for his tale.
The novel revolves around Bobby Wabalanginy, whom we first meet as a daring young boy, dreaming of women and whales. When British colonists arrive to establish a make-shift settlement, the leader of the expedition, Dr. Cross, takes a shine to Bobby's adoptive father, Wunyeran, and the two men become friends. The Aborigine starts to learn English, visiting the main settlement and experiencing the thrill of travelling across the ocean waves, while Cross starts to learn about the Noongar, hoping to set an example of cooperation which will spread to other settlements in the colony.
As the story progresses, however, these initial promising beginnings are tested. When the job of keeping the relationship alive and strong passes on to the next generation, it soon becomes clear that the balance of power has shifted – where the uncomfortable colonists were initially outnumbered by the Noongar, the settlement eventually grows, just as the indigenous numbers begin to dwindle. Once this happens, the spirit of equal claims to the land is almost doomed to failure.
The charismatic Bobby, singer, dancer and linguist, is a member of the generation growing up throughout this period of change, still living with his people but able to pick up enough English to make himself very useful to the settlers. His experiences mirror those of his people, at first indispensable, then grudgingly tolerated, later expected to conform to the new cultural norms, imported from beyond the horizon. Bobby wants to believe in the newcomers (from whom he received his new name), hoping to straddle the divide between the two worlds, and he continues with this until the end, long after many people (on both sides) have given up the pretence of mutual respect.
While I enjoyed the book, I must confess that it didn't impress me as much as you would expect from a book which took out Australia's top literary prize. It dragged a little for me, and the lack of any real plot, other than the main idea of the change in fortunes of the two groups, left me feeling that there was something missing. However, That Deadman Dance is an entertaining novel and extremely fascinating from a cultural point of view. The reader is shown how the lack of understanding between the two groups repeatedly complicates their efforts to get along.
The Noongar constantly expect the new arrivals to be leaving again, regarding them as guests, albeit ones who may be stretching their welcome a little. The settlers become annoyed that the natives constantly beg (or steal) food, not realising that they themselves are doing the same when they hunt kangaroos and dig up wild vegetables. Even friendships like that of Cross and Wunyeran are unable to paper over the cracks of ethnic differences.
I'm sorry to hear that this didn't impress you as much as you expected. The rave reviews persuaded me to import a copy (at great expense :-() and so this is top of my TBR at the moment. I hope I enjoy it more than you did.
I have this sitting on my shelf to get to at some point!
The last Miles Franklin winner I tried to read was Carpentaria which I couldn't finish, so I am hoping this one will be different!
Marg – I've had a few good ones with 'Breath', 'Dirt Music' and 'The Time We Have Taken', so I have very high expectations 😉 This is enjoyable and easy to read for literary fiction (if that makes you feel better!).
Sounds like it would be a great book, if you wanted to understand Aussie history, culture etc. but just missed the mark as a novel. Shame as the premise sounds good.
Well don't write it off completely 🙂 I get the feeling that I may be in a minority here – I'll be interested to see what other people have said about this one.
PS Is it possible to enable subscribing by email on your blog, Tony?
As for the e-mail thing, done!
Oh good, cos I prefer subscribing by email too so now I can check on you more easily … though I had put you as a gadget on my iGoogle to watch you!
Anyhow, it sounds like I liked it more than you did too. My reading group was generally very positive. Two, though, didn't like it at all. The rest of us all liked it a lot though most of us did find it a bit slow in the middle and some thought all the detail about whaling wasn't necessary. I could see what they were saying – and it was slow in the middle – but I didn't mind that. I liked the writing and description so much.
I think that sums it up nicely. For me, there was a little too much of the whaling etc, and it did drag in the middle.


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8 September 2010 Simulations for guiding the delivery and trapping of single biomolecules in a nanofluidic device
Lloyd M. Davis,	William N. Robinson
Lloyd M. Davis,1 William N. Robinson1
1The Univ. of Tennessee Space Institute (United States)
Proceedings Volume 7750, Photonics North 2010; 775005 (2010) https://doi.org/10.1117/12.871823
Event: Photonics North 2010, 2010, Niagara Falls, Canada
A microfluidic device has been developed wherein single molecules in solution are electrokinetically transported along a nanochannel. The nanochannel is irradiated by two adjacently focused laser beams so that the timing of fluorescence photons induced by each beam indicates the position of a molecule along the nanochannel. This is then used to actively control the electrokinetic flow, so that the molecule may be held within the confocal volume for a prolonged time and then rapidly replaced following photobleaching or completion of the single-molecule measurement. Here we focus on Monte Carlo computer simulations of the physical processes that occur during the delivery and trapping. The simulations help in understanding the constraints imposed by experimental limitations, such as the latency of feedback, the maximum achievable speed of electrokinetic flow, and photophysical processes such as triplet crossing and photobleaching. They also aid in evaluating the effects of shot noise and photon timing error and in predicting optimum experimental operating parameters. Studies indicate that the 6 μs latency of feedback in our experiments is well below that required for stable trapping (~100 μs); for small freely diffusing molecules, a limited flow speed of ~2 μm/ms can result in ~10-20 % of molecules escaping before they photobleach; there is an optimum laser power of ~30-40 μW that provides a sufficient rate of fluorescence photons for trapping while reducing loss due to photobleaching; an increase in the spacing between the beams or increase in relative power of the down-stream beam increases the trapping time.
Lloyd M. Davis and William N. Robinson "Simulations for guiding the delivery and trapping of single biomolecules in a nanofluidic device", Proc. SPIE 7750, Photonics North 2010, 775005 (8 September 2010); https://doi.org/10.1117/12.871823
Molecular lasers
Monte Carlo methods
Computer simulations
Field programmable gate arrays
Improved timing and diffusivity measurement in single molecule recycling in...
Maximum likelihood position sensing and actively controlled electrokinetic transport for...
Computer simulation of gene detection without PCR by single molecule...
Plasma membrane microorganization of LR73 multidrug resistant cells revealed by...
Interrogating unsupported lipid bilayers with scanning confocal single molecule fluorescence...
Lloyd M. Davis, William N. Robinson, "Simulations for guiding the delivery and trapping of single biomolecules in a nanofluidic device," Proc. SPIE 7750, Photonics North 2010, 775005 (8 September 2010); https://doi.org/10.1117/12.871823