Your ideas and insights over the past year have contributed to a more substantial Entrepreneurial Ecosystem Building Playbook. Thank you. This version – draft 2.0 – incorporates all we learned from you. We hope it provides a framework for people who seek to grow entrepreneurship in their communities, by providing more specific ideas, guidance, and insights on how to create more inclusive, dynamic, and diverse ecosystems.
Over the past year, we conducted hundreds of discovery discussions to understand the needs of entrepreneurs and those who champion entrepreneurs. This work confirmed many ideas in the first draft of this Playbook. More importantly, it gave us deeper insights into the needs of ecosystem builders and the challenges of ecosystem building.
These conversations reaffirmed our commitment to advancing entrepreneurial ecosystems as a model for economic development. That model focuses on how the whole of a community is far greater than the sum of its parts. It means starting with what a community has and connecting the pieces. More than ever, we believe ecosystem building can transform communities of varying sizes, demographic and socioeconomic contexts, and geographies, and create more sustainable economies everywhere.
We also learned, however, that people building ecosystems can't succeed on their own. The challenges of this work are significant. Doing it well requires a comprehensive approach, including sophisticated research, interlinked networks, collective learning, and institutional support.
Unfortunately, the emerging field of ecosystem building does not yet have all the necessary professional resources in place. We still lack consensus around some core aspects of the work, including shared outcomes, common metrics, proven methods, and key definitions. As a result, a growing number of ecosystem builders struggle with small budgets, overwhelming workloads, and skepticism in their communities. We see troubling levels of burnout among many practitioners.
Going forward, we must properly equip ecosystem builders as professionals to tackle the challenges they face. The Kauffman Foundation can help, but we can't do it without you. Together, we can assist practitioners and providers to build professional networks; share knowledge, practices, and stories; build new resources; and generate support for ecosystem building nationwide. This is ultimately about lifting you, the individuals doing the work on the ground and in the trenches.
Facilitate easier implementation on the ground in more communities.
Together, we can professionalize the practice of building entrepreneurial ecosystems. We can drive adoption of a new entrepreneur-centric approach to economic development. The effort will take time, and it will require extraordinary collaboration by all of us. And remember, we are still at the beginning. But succeed we must. In the balance are the silent hopes of countless entrepreneurs, yearning to turn their dreams into economic realities and contribute to our shared prosperity.
We are grateful to all of you doing this essential work. We've got your backs. And it's an honor to serve alongside you. Now, let's get to work!
In June 2017, 450 early leaders in entrepreneurial ecosystem building, from 48 U.S. states, Puerto Rico, the District of Columbia, and 10 other countries, met at the inaugural ESHIP Summit, the first of several convenings hosted by the Kauffman Foundation and 50+ national resource providers. Our mission: advance ecosystem building as a new approach to economic development to help more people and communities achieve economic independence through entrepreneurial success.
478 one-on-one discussions with ecosystem builders.
Theme 1: Across the field, there is shared hope to leverage the ecosystem building approach to create meaningful change for disconnected entrepreneurs. More diverse representation in leadership, case studies on effective practices, inclusion training, and resource development are critical next steps for this effort.
Make a significant impact in their communities.
Theme 2: The work of ecosystem building is not new, but it is also not yet widely understood or adopted.
Many innovators of the field began developing ideas and practices for ecosystem building approaches more than 20 years ago.
The field has not coalesced around a shared set of desired outcomes for ecosystem building, definitions for its key terms, or a research agenda to develop evidence-based approaches and metrics of community impact.
The tensions and competitive culture among ecosystem builders, established entrepreneur resource providers, and economic development leaders are creating additional barriers to supporting entrepreneurs.
Little voice in policy decisions that affect the entrepreneurial environment.
Collaborative research, policy advocacy, and community storytelling initiatives.
Promote, facilitate, and support field-wide collaborations to address identified needs.
At the 2018 ESHIP Summit, we will reconvene participants from the first event and bring in new contributors. We will reflect on the discussions over the last year and design our collective action to fortify the ecosystem building field and advance a more inclusive approach to the work. Chapter 4 of this Playbook puts forth a shared path for the work ahead.
This book is the first few chapters in what will become a more comprehensive playbook for the emerging field of entrepreneurial ecosystem building.
We are publishing this draft version of the playbook at the ESHIP Summit, not as the definitive answer on how to do our collective work, but to start a conversation with you about new and better ways to build entrepreneurial ecosystems everywhere.
We aim to co-create this book over time with you, the leaders and catalysts who work with grit, grace, and heart to help your community discover what is often a new way to co-create itself.
This draft Playbook will lead us to a more comprehensive and easy-to-use toolkit aimed at helping communities everywhere unlock their entrepreneurial potential.
We are thrilled you have chosen to join us on this journey, and we hope you will not hesitate to share your ideas, suggestions, constructive criticism, and stories about what you're learning along the way.
We believe that there is a need for a new model for inclusive economic development, one that takes into account the whole system.
We know that to build these complex yet accessible systems, we must act in interdependent, inclusive, and equitable ways.
We understand that these living systems evolve in complex ways, and that without collectively-held intent, they cannot grow to their full natural potential.
We recognize that to surface this collectively-held intent, there is a new emerging role, profession, and community of practice that is required – that of the ecosystem builder.
We are mindful that as an emerging field, these ideas will be new to many people. We must practice grace, humility, and empathy as we work with each other to evolve these new ways of thinking.
We embrace that we are a large group of leaders and catalysts with diverse backgrounds, skills, experiences, expertise, motivations, and visions for the future.
We are encouraged that in a world of increasing complexity, it is the inclusion of these diverse perspectives that will allow us to innovate, design, and deliver sustainable solutions for our ever-evolving communities.
We, as a community of practice, are here to support the development of 1) each other; 2) each others' communities; and 3) the principles, culture, tools, and metrics necessary to infuse entrepreneurship more deeply into our economics.
We acknowledge that change is difficult and requires sacrifice, and that growing healthy ecosystems often requires balancing the maximization of short-term outputs against a need to work with a long-term view. These challenges are essential – and important – to our success.
We pledge to pursue this vision together – to ensure that the makers, the doers, and the dreamers from all demographics of society have the opportunity to change their circumstances and to have a positive influence on themselves and the world.


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Homegrown love for Cardiff
TheDiff.blog from I Loves The 'Diff, the award winning cult brand. Keep it Cardiff, yeah?
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Spotlight on Womanby Street
ilovesthediff	Cardiff Events, Cardiff Pubs, Echo Column Articles, Uncategorized	 August 11, 2017 August 11, 2017 4 Minutes
This article – or a heavily edited version of it – was first published in March 2017 in the South Wales Echo. We do a regular column, see. They're listed by yer, they are. This month I Loves The 'Diff was basking in the glory of a fairly prestigious award and we jumped on the Save Womanby Street bandwagon
We've been doing this monthly column for nigh-on two years now. Hopefully in this time we've highlighted some fun events or delved into some Cardiff-related issues and so conveyed to you some new information that you've deemed to be useful.
Time certainly flies when you're having fun. Take the BBC in the Bay, for example. Did you know that this month it's celebrating five years of Roath Lock Studios, home to Doctor Who, Casualty and Pobol Y Cwm? Since opening in 2012 the 175,000 square foot facility has produced over 850 hours of TV, which have garnered 39 major industry awards. Better still, 52 apprentices have been trained at the site.
I was lucky enough to be invited along on a tour on Monday. Sadly, a bout of man put pay to my attendance so this is where the BBC part of the column ends. I did visit the Casualty set last year though, which was a lot of fun – it's useless if you're sick or injured but otherwise it's brilliant.
One of my favourite Cardiff streets is that of Womanby – a red brick and torn poster clad lane of drizzle-slickened cobblestone that that oozes street cred.
Home to some of the city's best indie venues, including of course the legendary Clwb Ifor Bach, Womanby Street is the vibrant hub of Cardiff's live music scene.
(Quick aside: many years ago I was once refused entry due to my inability to siarad Cymraeg. Absolutely scandalous, even if it was Welsh speaking night. Wouldn't happen today, of course. This was way back before social media, when woolly mammoths still roamed Bute Park, so I could only moan to the barman in The City Arms about it, and I don't think he was all that sympathethic if truth be told.)
Anyway, back to wonderful, gritty Womanby Street… In the words of Rob Toogood of Fuel Rock Club, "Womanby Street is a special place and there's nowhere like it in Cardiff." He's not wrong.
Last month, I wrote about the loss of Dempseys, soon to be a Brains and Bale sports bar and grill, which I hope does well, especially given its location. If that is the first sign of Womanby Street's gentrification then the second – approved plans by Wetherspoon's, owners of The Gatekeeper pub to create a 17-room upstairs – has prompted fears that such a development may mean the demise of what has made Womanby Street Cardiff's preminent alternative live music for the past 30 years or more.
In response to Wetherspoon's being granted planning permission – against which there was a petition with some 7,000 signatures – a campaign called Save Womanby Street launched last Sunday.
"Save Womanby Street was started to protect one of Cardiff's most important cultural music scenes. The addition of the hotel is a welcome benefit to the street, however under the current system of law, the venues could be threatened and even face closure," says Ewan Moor, co-manager of the campaign, which is working closely with Clwb Ifor Bach, the Full Moon, Fuel Rock Club, Tiny Rebel Cardiff and The City Arms.
Time to step up
Recent history suggests Ewan's fears are far from groundless. In February 2009, a great live venue, The Point, in Mount Stuart Square was forced to close after six years when the owner ran into financial difficulty following refurbishment work. As I understand it, around £100,000 had to be spent on soundproofing because of complaints about noise from occupants of newly built nearby flats.
"The aim of the campaign is to amend the Local Development Plan to make Womanby Street a 'cultural night time economy', which will protect the venues and allow them to continue to flourish," says Ewan, who works at The City Arms.
"We will be asking people to lobby their local Councillors in the coming weeks to support the amendment to the Local Development Plan. This is the best way to ensure our street survives and prospers. Change does not happen overnight. Change requires effort. In order to protect Womanby Street we need people's support and passion. It's time to step up."
…And party. Save Womanby Street plans to put on several events to highlight the cause, which are sure to be great fun. Follow @savewomanbyst on Twitter or search Facebook.
Last week was the Cardiff Life Awards, held at City Hall, co-hosted by everyone's favourite referee Nigel Owens. We dug out the glad rags because I Loves The 'Diff was up for awards in a couple of categories and very proud to be considered Finalists we were too.
As it happens we lost out to Jessica Draws in the creative category and, in the retail category, to Leekes (talk about David and Goliath!). I'd like to say many congratulations to them, as well as all the night's winners, including The Cardiff Story, Cardiff Market's Cheese Pantry, Boulders climbing centre, The Dead Canary bar, and the Danish bakery Brød.
It was a fun and glamourous night topped off by the wholly unexpected surprise of I Loves The 'Diff being awarded the very last and seemingly unscheduled award of the evening, that of 'Special Achievement'. I was quite literally speechless so I was relieved when I wasn't forced to make one.
It's a lovely feeling to receive such an acknowledgement from peers. I'm very grateful to the judges, and to everyone who clapped and high-fived me as I stumbled off the stage.
Visit ilovesthediff.com to see original ways to celebrate Cardiff
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\section{Introduction} \label{sec::Intro}

The volume of data generated daily has seen exponential growth in recent years \cite{patrizio2018idc}, coming from a number of sources including video streams, images, sensor streams, and text data. However, this large data volume is often redundant, and processing it is a non-trivial and expensive task. Extracting meaningful information through summarization can make it easier to process by downstream tasks. Extractive summarization can be viewed as a constrained subset selection problem.

Submodular functions are a natural choice for summarization due to their diminishing returns property, since they naturally model aspects like diversity, coverage and representation. As a result, they have been used in various data summarization problems like image collection summarization \cite{tschiatschek2014learning, simon2007scene}, video summarization ~\cite{zhang2016video,Gygli2015VideoSB,Kaushal2019DemystifyingMV,Kaushal2019AFT, kaushal2019framework, kaushal2020realistic}, document summarization ~\cite{lin2011class,lin2012learning,li2012multi,chali2017towards,yao2017recent}, and data selection~\cite{wei2015mixed,wei2015submodularity,wei2014submodular,kaushal2019learning}. Most work on learning submodular models has either focused on very simple models such as mixtures of submodular functions~\cite{lin2012learning,tschiatschek2014learning,gygli2015video}, or are rich but restricted to sub-classes of functions (like log-determinants~\cite{kulesza2012determinantal} or nested concave over submodular functions~\cite{dolhansky2016deep}). On the other hand, with the success of deep learning based methods, deep features have replaced many older techniques using hand-crafted features due to their ability in capturing semantic information through feature interactions. This has led to the use of deep models in various summarization problems \cite{fu2019attentive, zhang2016video}. Deep models however, do not model interaction between instances required for modelling diversity, representation and coverage.  In this paper, we combine the best of both worlds by jointly learning submodularity and feature interactions leading to richer and deeper summarization models.

 Formally, a submodular function is defined as follows. A set function $f: 2^V \rightarrow \mathbf{R} $, defined over all subsets of a ground set $V = \{v_1,v_2, \cdots ,v_n\}$ (images, sentences, or video frames), is \textit{submodular} if for $x \in V$, $f(A \cup x) - f(A )\geq f(B \cup x) - f(B)$, $\forall A \subseteq B \subseteq V$ and $x \notin B$. For a set $A \subseteq V$, $f(A)$ provides a real-valued score for A. In the context of summarization, $f(A)$ indicates the quality of summary defined by set A. A function $f$ is said to be monotone if $f(A) \leq f(B)$ whenever $A \subseteq B$. Also, $f$ is \textit{supermodular} if $- f$ is \textit{submodular}, modular if it is both, and \textit{normalized} if
$f(\phi) = 0$.

Any submodular function based summarization procedure consists of at least two parts: 1) Defining the objective submodular function and 2) performing a constrained maximization on the function. Even though constrained submodular maximization is NP-hard, there are a number of constant factor approximations available ~\cite{minoux1978accelerated, mirzasoleiman2014lazier, mirzasoleiman2016fast, nemhauser1978analysis}. However, the optimal design of the objective function for a particular problem is a non-trivial task, and even learning the submodular function from data is extremely hard and not easy to even approximate~\cite{balcan2011learning}. However, learning can be made easier by restricting the scope of the submodular function. In this work, we present a rich and powerful, yet tractable framework called Deep Submodular Networks (DSNs) which enables joint learning of features and function parameters in an end-to-end and tractable manner. Thus, learning using DSN amplifies the modelling power by allowing us to represent various complex submodular functions. Moreover, the DSN framework has the following characteristics: 1) can be applied to any summarization task by parameterizing submodular functions, 2) can be operated on multi-modal data if the representation can be done in the same feature space (as in query focused summarization) 3) allows warm-start by instantiating functions using features from a pre-trained network 4) reduces computational complexity by learning complex representation using fewer components that would otherwise need many components. We then study the parameter learning problem with DSNs, and propose a max-margin based learning algorithm. Finally, we present results on the task of summarizing an image collection. Two flavours of summarization are considered: 1) generic and 2) query-focused. The results demonstrate that mixture learning using DSN is effective.

\section{Related Work and Our Contributions}
 \subsection{Related work on Extractive Data Summarization: } The problems of image, video, and document summarization have been extensively explored in literature. For image collection summarization \cite{simon2007scene} used the facility location function with a diversity penalty term and \cite{sinha2011extractive} propose a diversity model by using a coverage function and a disparity function. Other approaches include hierarchical clustering \cite{jaffe2006generating} and k-medoids-based clustering~\cite{zhao2016visual}, dictionary learning~\cite{yang2013image}, and deep learning~\cite{ozkose2019diverse}. \cite{tschiatschek2014learning} was among the first to learn the weights of a mixture of submodular functions using max-margin learning. A related approach is the use of $k$-DPPs~\cite{Kulesza2011kDPPsFD}. \cite{kaushal2020unified} extends the dataset of \cite{tschiatschek2014learning} for query focused summarization, which we use for our experiments. Similar to image summarization, submodular optimization is vastly used for video summarization~\cite{zhang2016video,Gygli2015VideoSB,Kaushal2019DemystifyingMV,Kaushal2019AFT} and document summarization~\cite{lin2011class,lin2012learning,li2012multi,chali2017towards,yao2017recent}. Other diversity-based approaches for video summarization include using Maximum Marginal Relevance (MMR) \cite{li2012multi}, and Determinantal Point Processes (DPPs) for summarization~\cite{zhang2016video, gong2014diverse, kulesza2012determinantal}.

\subsection{Related work on Query Focused Data Summarization: } While most literature has focused on generic summarization, a recent work employs submodular functions to parametrize submodular mutual information measures for query-focused summarization \cite{kaushal2020unified}. \cite{lin2011class} achieves state-of-the-art results for query-focused document summarization by defining a joint diversity and query relevance term. In video summarization, \cite{vasudevan2017query} study a query relevance term based on graph-cut function and ~\cite{sharghi2016query,sharghi2017query} study hierarchical DPPs to model both diversity and query relevance. Graph-cut functions are also used by \cite{li2012multi} for query focused and update summarization. All the above work on query-focused summarization use instances of submodular information measures. In our work, we use a general parametrized form of submodular mutual information measures as proposed by \cite{kaushal2020unified}.

\subsection{Related work on Learning Submodular Functions: } Since submodular functions lie in a vast space with $2^n$ degrees of freedom, it is non-trivial to find the right submodular function for a given problem. Hence, it is imperative to learn submodular functions based on queries or data. However, many theoretical results show that learning submodularity can be very hard in the worst case -- for example, learning general submodular in the PMAC setting is not approximable to any polynomial factor in the worst case \cite{balcan2011learning}. The problem becomes easier if we restrict ourselves to simpler classes of functions. One example of this is by defining a simple mixture of submodular functions~\cite{tschiatschek2014learning, kaushal2019framework, kaushal2020realistic, sipos2012large, lin2012learning,lavania2019auto} by constructing a weighted convex combination of non-negative submodular functions $F_w(A) = \sum_{i=1}^M w_if_i$, where $f_1, f_2, \cdots ,f_M$ are submodular functions with weights $w = (w_1, w_2, \cdots, w_M)$, $w_i  \geq 0$, $\sum_i w_i = 1$. Here, the mixture's components $f_i$ are fixed (instantiated using features from a pre-trained network), and only the mixture weights are learned.  However, this limits the modelling power of $F_w(.)$ because the feature interactions and the functions' internal parameters are not learned. To mitigate this, recent work on a richer parametric family of submodular functions called deep submodular functions or DSFs \cite{dolhansky2016deep, bilmes2017deep} explores a new class of functions with characteristics similar to a DNN due to its multilayered architecture.  They try to address this by parameterizing $f_{\hat{w}} \in DSF$ where the vector $\hat{w}$ determines the network topology and the concave functions. DSFs employ a recursive application of concave over modular functions, thereby maintaining submodularity.
Although DSFs boost the modelling power of submodular functions, they are still a restricted subclass of functions and do not effectively learn the feature representations in an end to end manner. As a result, DSFs often perform worse in practice compared to mixtures of many submodular functions (as we see in our experiments).

\subsection{Our Contributions}
The main contributions of this work are as follows. We present Deep Submodular Networks (DSNs) which enables joint learning of the model parameters and feature representations. We then study the parameter learning problem for DSNs via max-margin learning and study the properties of the resulting algorithm. We also show how the DSNs for different submodular functions can be combined either via mixtures of submodular functions or via compositions of submodular functions, similar to deep submodular functions. We then empirically demonstrate the effectiveness of DSN for data summarization by drawing various insights. a) We first show that DSN can learn superior features that, when used to instantiate submodular functions, generate summaries with higher V-ROUGE scores compared to submodular functions instantiated using features from a pre-trained DNN. b) We next show that using a simple mixture of four DSNs significantly outperforms existing baselines including a mixture of four submodular functions (without learning the feature interactions), the deep submodular functions~\cite{dolhansky2016deep}, and differentiable submodular maximization~\cite{tschiatschek2018differentiable}. Furthermore, we also show that just using 4 DSN components, we are able to achieve comparable performance to a mixture model of 594 hand-crafted components. To the best of our knowledge, an end-to-end learning framework like DSN has not been studied before for any summarization task.

\section{Submodular Functions for Summarization}

We focus on monotone, non-negative submodular functions for use as components in the submodular mixture. These mixture components will belong to one of the following types of submodular functions, depending on the type of summarization task in hand. For a more detailed summary of submodular functions used, see~\cite{iyer2015submodular,iyer2015spp,kaushal2020unified,iyer2020submodular}.

\subsection{Generic Summarization}

In this paper, we focus on generic extractive summarization under a cardinality constraint, the optimization problem is:  $\max_{A: |A| \leq k}  f(A)$ for a given submodular function $f$ and budget $k$. Submodular functions for this flavour of summarization are mostly instantiated using either a similarity matrix $S$ that stores the similarity between data points, or directly using the feature representation of data points. Let $V$ be the ground set of data points which need to be summarized. We denote $A \subseteq V$ as a candidate subset of the data points in $V$. For any two data points $i,j \in V$, we denote $s_{ij}$ to be the similarity between them.\\
\noindent\textbf{Facility Location: } The facility location function can be defined as:
\begin{align} \label{eq:FL}
f_{FL}(A) = \sum_{i \in V} \max_{j \in A} s_{ij}
\end{align}
An additional penalty term $\lambda$ can be added to get:
\begin{align} \label{eq:FLP}
f_{FLP}(A) = \sum_{i \in V} \max_{j \in A} s_{ij} - \lambda \sum_{i,j \in A} s_{ij}
\end{align}

Instantiating kernel based functions like facility location function is computationally expensive as it requires the generation of the similarity values $s_{ij}$. The similarity calculation involved the computation of a $O(n^2)$ kernel matrix \cite{liu2013submodular}. Facility location function models representation, and is similar to k-mediod clustering and has been used in various summarization tasks \cite{simon2007scene, takamura2010text, xu2019deep}.

\noindent\textbf{Saturated Coverage: } This function models coverage of the ground set and is defined as:
\begin{align} \label{eq:SatCov}
f_{SAT}(A) = min(\sum_{i \in A} s_{ij}, \lambda \sum_{i \in V} s_{ij})
\end{align}
where $\lambda$ value controls the degree at which this function gets saturated. Saturated Coverage allows the selection of multiple representatives of every category, whereas facility location prefers only a single representative.

\noindent\textbf{Graph Cut: } The generalized graph cut is defined as:
\begin{align} \label{eq:GraphCut}
f_{GC}(A) = \lambda \sum_{i \in V} \sum_{j \in A} s_{ij} - \sum_{i,j \in A} s_{i,j}
\end{align}
This function is similar to saturated coverage and facility location in terms of its modelling behavior. The $\lambda$ parameter tunes the tradeoff between diversity and representation. As $\lambda$ increases, graph-cut tries to model diversity. For values of $\lambda < 0.5$ graph-cut is monotone submodular and for $\lambda > 0.5$ it is non-monotone submodular. It has been widely used for summarization tasks \cite{vasudevan2017query, li2012multi, lin2012submodularity}.

\noindent\textbf{Feature Based: } The feature based function assumes that each data element from the ground set $V$ can be represented in a feature space $U$. They have proved to be useful in a variety of summarization tasks ~\cite{ni2015submodular, wei2014submodular, tschiatschek2014learning, kirchhoff2014submodularity}. This function can be defined as:

\begin{align} \label{eq:FB}
f_{FB}(A) = \sum_{i \in U} \gamma_i\psi_i(m_i(A))
\end{align}

Here $m_i(A) = \sum_{a \in A} U_i^a$ with $U_i^a$ being the representation of data element $a$ for feature $i$. The functions $\psi_{i}(\cdot)$ are monotone non-decreasing concave functions associated with feature $i$ and $\gamma_i$ is the corresponding weight. The maximization of a feature based function attempts to find a subset $A$ that covers the maximum number of concepts in $V$.

\subsection{Query-focused Summarization}
Since generic summarization focuses on creating a general candidate summary by treating all data points in the ground set $V$ equally, it is impossible to incorporate user intent within a summary. Query focused summarization attempts to produce summaries relevant to the input query set $Q$ in addition to the characteristics of a good summary (such as representation, coverage, diversity). In this paper, we use the recently proposed framework of~\cite{iyer2020submodular,kaushal2020unified} which uses the submodular mutual information (SMI). SMI is a generalization to handle query relevance in a natural manner. Given a set $B \subseteq (V \cup Q)$, the submodular mutual information $I_f(A; B)$ is defined as: $I_f(A; B) = f(A) + f(B) - f(A \cup B)$. We define the optimization problem for cardinality constrained query-focused summarization to be $\max_{A \subseteq V: |A| \leq k}  I_f(A; Q)$ for a given submodular function $f: 2^{V \cup Q} \rightarrow \mathbf{R}$, query set $Q$ and budget $k$. In such a problem formulation, although $f$ is defined on $V \cup Q$, only items from $V$ are summarized, i.e, the discrete optimization will only be on subsets of $V$.
In addition to the similarity matrix $S$, we compute $S_Q$, which stores the similarity between each data point of $V$ with every query data point in $Q$. For any two data points $i \in V$ and $j \in Q$, we denote $sq_{ij}$ to be the similarity between them.

\noindent\textbf{Graph Cut Mutual Information:} The submodular mutual information (SMI) instantiation of graph-cut (GCMI) is defined as:
\begin{align} \label{eq:GCMI}
I_f(A;Q)=2\sum_{i \in A} \sum_{j \in Q} sq_{ij}
\end{align}
Since maximizing GCMI maximizes the joint pairwise sum with the query set, it will lead to a summary similar to the query set $Q$. In fact, specific instantiations of GCMI have been intuitively used for query-focused summarization for videos ~\cite{vasudevan2017query} and documents ~\cite{lin2012submodularity, li2012multi}.

\noindent\textbf{Facility Location Mutual Information - V1:} In the first variant of FL, we set $D$ to be $V$. The SMI instantiation of FL1MI can be defined as:
\begin{align} \label{eq:FL1MI}
I_f(A;Q)=\sum_{i \in V}\min(\max_{j \in A}s_{ij}, \lambda \max_{j \in Q}sq_{ij})
\end{align}
The first term in the min(.) of FL1MI models diversity, and the second term models query relevance. An increase in the value of $\lambda$ causes the resulting summary to become more relevant to the query.

\noindent\textbf{Facility Location Mutual Information - V2:} In the V2 variant, we set $D$ to be $V \cup Q$. The SMI instantiation of FL2MI can be defined as:
\begin{align} \label{eq:FL2MI}
I_f(A;Q)=\sum_{i \in Q} \max_{j \in A} sq_{ij} + \lambda\sum_{i \in A} \max_{j \in Q} sq_{ij}
\end{align}
FL2MI is very intuitive for query relevance as well. It measures the representation of data points that are the most relevant to the query set and vice versa. It can also be thought of as a bidirectional representation score.

\section{Deep Submodular Networks}
This section presents an efficient learning framework to learn a mixture of submodular functions in an end-to-end manner. Learning mixtures of submodular functions has given good empirical results on various summarization tasks including image \cite{tschiatschek2014learning}, video \cite{kaushal2020realistic} and document  summarization \cite{lin2012learning}. They use a max-margin learning framework for effectively learning the weights of the mixture. The weights are optimized such that the learned function tries to mimic characteristics of ground truth summaries (typically human-generated) based on a given loss. This is done by computing a gradient that requires minimizing the difference between two submodular components (one is the mixture and other is the loss) \cite{tschiatschek2014learning}. In our work, we apply the max-margin approach for learning the parameters of DSN end-to-end. We now describe the architecture and learning of DSNs.

\subsection{Architecture} \label{sec::DSNArch}

Figure~\ref{fig:dsn} describes the three-layer architecture of DSN. In the first layer, given a set of data points, which can be either be represented by using features from a pre-trained DNN or by raw features, we compute its DSN feature representation by passing it through a feature extractor which is parameterized by $\theta$. Here,  $\theta$ is a common parameter for all the DSN components and is responsible for learning feature interactions. As discussed above, submodular functions can be instantiated by using features (as in feature based functions) or by a similarity kernel (as in graph submodular functions). In addition to $\theta$, the submodular functions are parameterized using their internal parameters $\lambda_i$ in the second layer of DSN. For instance, $\lambda_i$ can be the diversity-representation tradeoff parameter in graph-cut or the saturation weight in saturated coverage or the query-relevance tradeoff in FLMI functions. The last layer is a convex combination of these submodular functions parameterized by $\theta$ and $\lambda$, where the mixture weights are parameterized by $w_i$. For generic summarization, we define the DSN mixture model as :
\begin{align} \label{mixture-dsn-gen}
    F(A, x, w, \theta, \lambda) = \sum_{i=1}^M w_i f_i(A, x, \theta, \lambda_i)
\end{align}
Here $x$ is the raw features of the input instances (for e.g. a video or image collection). For query-focused summarization, a Query set $Q$ can be incorporated in this model by defining a DSN over the submodular mutual information based functions:
\small{
\begin{align} \label{mixture-dsn-query}
    F(A, Q, x, w, \theta, \lambda) = \sum_{i=1}^M w_i I_{f_i}(A; Q, x, \theta, \lambda_i)
\end{align}}
\normalsize
The DSN optimization problem for summarization is:
\begin{align}
A \in \argmax{A \subseteq V, |A| \leq k} F(A)
\end{align}
where $F$ is defined in equations~\eqref{mixture-dsn-gen} and \eqref{mixture-dsn-query}.

Finally, we can also consider more complex ways of combining the DSNs as opposed to just simple mixtures. In particular, since concave over monotone submodular functions are submodular~\cite{dolhansky2016deep}, we can combine them recursively using an approach very similar to a DSF. Given concave function $\psi_i, i = 1, 2, \cdots, P$, define the following parameterized model:
\begin{align} \label{dsf-dsn-gen}
    F(A, x, w, \theta, \lambda) = \sum_{i = 1}^P \psi_i(\sum_{j=1}^M w_{ij} f_i(A, x, \theta, \lambda_i))
\end{align}
We can similarly define this for submodular mutual information functions (i.e. equation~\eqref{mixture-dsn-query}) for query based summarization. For completeness, we add it below.
\begin{align} \label{dsf-dsn-query}
    F(A,Q, x, w, \theta, \lambda) = \sum_{i = 1}^P \psi_i(\sum_{j=1}^M w_{ij} I_{f_i}(A;Q,x, \theta, \lambda_i))
\end{align}

Finally, note that we can also recursively define to have additional compositions of concave functions. In the rest of the paper, we will derive expressions for simple mixtures (i.e. equations~\eqref{mixture-dsn-gen} and \eqref{mixture-dsn-query}), but the expressions for the more general compositions (i.e. DSF-DSNs) can be obtained similarly.

\subsection{Learning Deep Submodular Networks} \label{sec::MaxMarginDSN}

In this paper, we consider the supervised summarization problem. In the case of generic (i.e. query agnostic) summarization, we assume we are given a dataset of $N$ training examples as $(Y^{(n)}, V^{(n)}, x^{(n)})$ where $n=1 \dots N$, $Y^{(n)}$ is a human summary for the $n^{th}$ ground set $V^{(n)}$ with off-the-shelf/raw features $x^{(n)}$. Also assume, we have an ideal loss function (equivalently evaluation function) $l : 2^V \times 2^V \rightarrow \mathbf{R}$
which measures how close an automatically generated summary is to a human summary. The goal of the learning problem is to learn parameters $\theta, w, \lambda)$ which solves the following empirical risk minimization problem:
\begin{align}\label{empriskmin}
    \min_{w, \theta, \lambda} \sum_{n = 1}^N l(\argmax{A \in C^{(n)}} F(A, x^{(n)}, w, \theta, \lambda), Y^{(n)})
\end{align}
Here the constraint $C^{(n)} = \{A \subseteq V^{(n)}, |A| \leq k\}$.
In the case of query based summarization, the setup is very similar~\cite{kaushal2020unified}. The training data here additionally has queries for every example: $(Q^{(n)}, Y^{(n)}, V^{(n)}, x^{(n)})$ where $n=1 \dots N$. The resulting learning problem is very similar to equation~\eqref{empriskmin} except that we use the query based model and therefore have the query as well.

While this is the \emph{true} learning problem we care about, it is very difficult to optimize this since this is a non-convex and has sharp discontinuities. As a result, we resort to max-margin based learning framework.

\begin{figure}[h]
    \centering
    \includegraphics[width=0.8\textwidth]{dsn_2.png}
    \caption{Deep Submodular Networks: A three layer architecture. First is the feature extractor module, which is a parameterized neural network. Second is the similarity kernel and feature aggregator modules to initlialize either the similarity based or feature based submodular functions with internal parameters $\lambda_i$.}

    \label{fig:dsn}
\end{figure}

Recall that in our model, $\lambda_i$ corresponds to the internal parameters of the submodular function $i$, and define the vector $\lambda = (\lambda_1, \dots, \lambda_M)$. Similarly,  $\theta$ represents the shared parameters of the learned features, and $w$ denotes the weight matrix (which is a simple vector in the mixture model case and a matrix in the DSF case). Let $\Theta = (w,\lambda, \theta)$ denote the vector of parameters of the DSN. The max-margin approach \cite{taskar2004max, sipos2012large, lin2012learning, tschiatschek2014learning, kaushal2020realistic} is used to  learn the parameters $\Theta$ of $F(A, x, \Theta)$. The goal of max-margin is to try to ensure that we learn parameters $\Theta$ such that the following inequality holds for every training example:
\small{
\begin{align}
    F(Y^{(n)}, x^{(n)}, \Theta) \geq \max_{A \in C^{(n)}}[F(A, x^{(n)}, \Theta) + l(A, Y^{(n)})]
\end{align}}
\normalsize
Following~\cite{taskar2004max, tschiatschek2014learning,lin2012learning}, we can formulate this as a loss-augmented inference based optimization problem. Define:
\begin{multline}
L_n(\Theta) = (\max_{A \in C^{(n)}} [\sum_{i=1}^M w_i f_i(A, x^{(n)}, \theta, \lambda_i) + l_{Y^{(n)}}(A)] - \sum_{i=1}^M w_i f_i(Y^{(n)}, x^{(n)}, \theta, \lambda_i)) +\frac{\beta}{2} ||w||_2^2
\label{mmleq}
\end{multline}
where, $\beta$ is the regularization parameter. Then the learning problem is: $\min_{\Theta} \sum_{n = 1}^N L_n(\Theta)$.

For the purpose of learning the parameters $w_i$ , $\lambda_i$ and $\theta$ we compute the subgradients for each of the parameters as follows,\\
\textbf{Sub gradient of mixture weights $w_i$:
\begin{align}
\frac{\partial L_n}{\partial w_i} = f_i(\hat{A}_n, x^{(n)}, \theta, \lambda_i) - f_i(Y^{(n)}, x^{(n)}, \theta, \lambda_i) + \beta_iw_i
\label{eq2}
\end{align}
\textbf{Sub gradient of function parameters $\lambda_i$:
\begin{align}
\frac{\partial L_n}{\partial \lambda_i} = w_i \frac{\partial f_i(\hat{A}_n, x^{(n)}, \theta, \lambda_i)}{\partial \lambda_i}  - w_i \frac{\partial f_i(Y^{(n)}, x^{(n)}, \theta, \lambda_i)}{\partial \lambda_i}
\label{eq3}
\end{align}
\textbf{Sub gradient of common feature parameters $\theta$:
\begin{align}
\frac{\partial L_n}{\partial \theta} = w_i \frac{\partial f_i(\hat{A}_n, x^{(n)}, \theta, \lambda_i)}{\partial \theta}  - w_i \frac{\partial f_i(Y^{(n)}, x^{(n)}, \theta, \lambda_i)}{\partial \theta}
\label{eq4}
\end{align}

where, $\hat{A}_n = \argmax{A \in C^{(n)}} F(A, x^{(n)}, \theta, w, \lambda) + l_{Y^{(n)}}(A) $

We summarize our learning methodology in algorithm \ref{alg:dsnlearn}.

\subsection{Subgradients of submodular functions}

\subsubsection{Generic Summarization}
Consider an entry $s_{ij}$ from the similarity matrix $S$ which is computed from DSN feature representations $X_i=\sigma(\theta^T \cdot U_i)$ and $X_j= \sigma(\theta^T \cdot U_j)  $, where $U_i$ and $U_j$ represent off-the-shelf/raw features of data point $i$ and $j$ respectively. Let us assume that that $X_i$ and $X_j$ are L2 normalized. The cosine similarity between them is simply their dot product: $s_{ij} = X_i^T \cdot X_j$. We can rewrite $s_{ij} = \sigma(\theta^T \cdot U_i)^T \cdot \sigma(\theta^T \cdot U_j)$, where $\sigma$ is the sigmoid function. The subgradient of the similarity matrix $\triangledown_\theta s_{ij}$ can be computed as follows:

\begin{align}\label{eq: gradCalcFL4}
\triangledown_\theta s_{ij} = \sigma(\theta^T U_i)^T\sigma(\theta^T U_j)((1-\sigma(\theta^TU_j))^T U_j + (1- \sigma(\theta^T U_i ))^T U_i)
\end{align}

We compute $\triangledown_{\lambda}f$, the subgradient with respect to the respective internal parameters of individual function components, and  $\triangledown_\theta s_{ij}$, the subgradient with respect to parameters of learned features. These subgradients are computed for each of the functions used in the mixture as follows:

\noindent\textbf{Facility Location with Penalty:} From \ref{eq:FLP}, we get:
\begin{equation}
\begin{aligned}
\triangledown_\theta f_{FLP} &= \sum_{i \in V}\triangledown_\theta s_{ij_i} - \lambda \sum_{i,j \in S} \triangledown_\theta s_{ij}\\
\triangledown_\lambda f_{FLP} &= -\sum_{i,j \in S} s_{ij}
\end{aligned}
\end{equation}

\noindent where, $j_i = \argmax{j \in S}s_{ij}$\\

\noindent\textbf{Saturate coverage:} From \ref{eq:SatCov}, we get:
\begin{equation}
\begin{aligned}
\triangledown_\lambda f_{SC} &= \sum_{i,j \in V}s_{ij}*1_{\lambda \sum_{j \in V} s_{ij} \leq \sum_{j \in A} s_{ij}}\\
\triangledown_\theta f_{SC} &= \sum_{i \in V}(\sum_{j \in A} \triangledown_\theta s_{ij}*1_{ \sum_{j \in A} s_{ij} \leq \lambda \sum_{j \in V} s_{ij}} + \lambda \sum_{j \in V} \triangledown_\theta s_{ij}*1_{\lambda \sum_{j \in V} s_{ij} \leq \sum_{j \in A} s_{ij}})
\end{aligned}
\end{equation}

\noindent\textbf{Graph Cut:} From \ref{eq:GraphCut} we get:
\begin{equation}
\begin{aligned}
\triangledown_\lambda f_{GC} &= \sum_{i \in V} \sum_{j \in A} \triangledown_\theta s_{ij}\\
\triangledown_\theta f_{GC} &= \lambda \sum_{i \in V} \sum_{j \in A} \triangledown_\theta s_{ij} - \sum_{i,j \in A} \triangledown_\theta  s_{i,j}
\end{aligned}
\end{equation}

\noindent\textbf{Feature based:} From \ref{eq:FB} and assuming $\phi(.)$ as $\sqrt{.}$ we get,\\
\begin{equation}
\begin{aligned}
f_{FB}(A) &= \sum_{i \in U} \gamma_i\psi(\sum_{a \in A} \sigma(U_i^a \theta_i))\\
\triangledown_{\gamma_i}f_{FB} &= \psi_{i} (m_i(A))\\
\triangledown_\theta f_{FB} &= \sum_{i \in U} \gamma_i\frac{\sum_{a \in A} \sigma(U_i^a \theta_i) (1 - \sigma(U_i^a \theta_i)) U_i^a}{2\sqrt{\sum_{a \in A} \sigma(U_i^a \theta_i)}}
\end{aligned}
\end{equation}

\subsubsection{Query-focused summarization}
Consider an entry $sq_{ij}$ from the similarity matrix $S_Q$ which is computed from the off-the-shelf/raw feature representation of query $q_i$ and DSN feature representation of data point $X_j=\sigma(\theta^T \cdot U_j)$. Assume that $X_j$ and $q_i$ are L2 normalized. Then, the cosine similarity between them is simply their dot product: $s_{ij} = q_i^T \cdot X_i$. We can rewrite $sq_{ij} =  q_j^T \cdot \sigma(\theta^T \cdot U_i)^T$, where $\sigma$ is the sigmoid function. The subgradient of the similarity matrix $\triangledown_\theta sq_{ij}$ can be computed as follows:

\begin{align}
    \triangledown_\theta sq_{ij} = q_i^T \sigma(\theta^T U_j )(1- \sigma(\theta^T U_j ))U_j
\end{align}
We compute the gradients wrt $\lambda$ and $\theta$ as follows:\\
\textbf{GCMI:} From \ref{eq:GCMI} we get,
\begin{align}
    \triangledown_\theta I_f (A;Q) = 2\sum_{i \in A} \sum_{j \in Q}  \triangledown_\theta sq_{ij}
\end{align}

\noindent\textbf{FL1MI:} From \ref{eq:FL1MI} we get:
\begin{equation}
\begin{aligned}
    \triangledown_\lambda I_f (A;Q) &= \sum_{i \in V} \max_{j \in Q} sq_{ij}*1_{\lambda sq_{ij_Q} \leq s_{ij_A})}\\
    \triangledown_\theta I_f (A;Q) &= \sum_{i \in V} \triangledown_\theta s_{ij_A} * 1_{s_{ij_A} < \lambda sq_{ij_Q}} + \lambda \triangledown_\theta sq_{ij_Q} * 1_{\lambda sq_{ij_Q} \leq s_{ij_A}}
\end{aligned}
\end{equation}
where, $j_A = \argmax{j \in A} s_{ij}$ and $j_Q = \argmax{j \in Q} sq_{ij}$

\noindent\textbf{FL2MI:} From \ref{eq:FL2MI} we get,
\begin{equation}
\begin{aligned}
    \triangledown_\lambda I_f (A;Q) &= \sum_{i \in A} \max_{j \in Q} sq_{ij}\\
    \triangledown_\theta I_f (A;Q) &= \sum_{i \in Q} \triangledown_\theta  sq_{ij_A} + \lambda \sum_{i \in Y} \triangledown_\theta  sq_{ij_Q}
\end{aligned}
\end{equation}
 where, $j_A = \argmax{j \in A} sq_{ij}$ and $j_Q = \argmax{j \in Q} sq_{ij}$

\begin{algorithm}
\caption{DSN Learning}
\label{alg:dsnlearn}
\begin{algorithmic}[1]
\STATE \textbf{Input:} $N$ Training examples $\{X^n\}_{n=1}^N$ and their ground truth summaries $\{Y^{(n)}\}_{n=1}^N$, M submodular functions $\{f_i\}_{i=1}^M$\\
\STATE \textbf{Initialize} $\Theta = (w_1 \dots w_M, \lambda_1 \dots \lambda_M, \theta)$
\WHILE{Not Converged}
\STATE{For fixed $( \lambda_1 \dots \lambda_M, \theta)$ update $(w_1 \dots w_M)$ by computing subgradients using \ref{eq2}}
\STATE{For fixed $(w_1 \dots w_M)$ update  $( \lambda_1 \dots \lambda_M, \theta)$ by computing subgradients using \ref{eq3} and \ref{eq4}}
\ENDWHILE\\
\textbf{Output: } Optimal DSN parameters $\Theta$
\end{algorithmic}
\end{algorithm}

\section{Empirical Experiments for Learning using DSN}

In this section, we evaluate the performance of our method, which we refer to as Deep Submodular Networks (DSN), and compare it with 1) submodular mixture model \cite{tschiatschek2014learning}, 2) Deep Submodular Functions (DSF)~\cite{dolhansky2016deep}, Differentiable Submodular Maximization (DSM)~\cite{tschiatschek2018differentiable}, and individual submodular functions. We use the data set of \cite{tschiatschek2014learning} (and its extension~\cite{kaushal2020unified}) consisting of 14 distinct real-world image collections with 100 images per collection. The image summarization problem is to select a summary set of 10 images that best represent the collection. We train the DSN on each of the 14 collections by using a leave-one-out-cross-validation scheme \cite{hastie2009elements} on the remaining 13 collections. We use the max-margin approach for training where the loss function is $l(A) = 1-r(A)$ and $r(A)$ is V-ROUGE (as defined in section 2.2 of \cite{tschiatschek2014learning}). For evaluation, we use a normalized version of V-ROUGE where the average normalized V-ROUGE score of random summaries is 0, and that of human summaries is 1.

\subsection{Generic Summarization}
We used a DSN of the form $F(A, x^{(n)}, w, \theta, \lambda) = \sum_{i=1}^M w_i f_i(A, x^{(n)}, \theta, \lambda_i)$. For the parameterized feature embedder, we use a hidden layer of 512 units of sigmoid activation whose weights are initialized using Xavier initialization \cite{glorot2010understanding}. The similarity kernel $S$ is computed using cosine similarity. For our experiments, we initialize the DSN with either a single fucntion or a mixture of four submodular functions - namely, graph-cut (\ref{eq:GraphCut}), saturate coverage (\ref{eq:SatCov}), facility location (\ref{eq:FL}) or feature based (\ref{eq:FB}). The mixture weights are randomly initialized and the DSN was trained end-to-end using Adam and a decaying learning rate.

\begin{figure}[h]
    \centering
    \includegraphics[width=0.5\textwidth]{generic_dsn.PNG}
    \caption{Performance of DSN for Generic Summarization on Test data.}
    \label{fig:generic_dsn}
\end{figure}

\begin{figure}[h]
    \centering
    \includegraphics[width=0.9\textwidth]{dsn_plot_2.PNG}
    \caption{Learning and generalization of DSN. Performance of - Left: Mixture Model with 4 components, Middle: facility location, Right: feature based function}
    \label{fig:dsn_plot}
\end{figure}

In the first set of experiments, we represent each image using features extracted from the pool5 layer of GoogleNet \cite{szegedy2015going} pre-trained on ImageNet \cite{deng2009imagenet}. We compare submodular functions instantiated using features from the above pre-trained DNN with submodular functions instantiated using learned features from a DSN. Figure \ref{fig:generic_dsn} shows that DSN learned features outperform the pre-trained DNN features for all submodular functions. This performance shows that DSN can indeed learn the features and individual parameters of the four submodular functions (FL, FB, GC and SC). Next, we repeat this for the mixture. We compare the mixture of DSNs learnt in an end to end manner, with a simple mixture model over the four submodular functions. Again, we see that Mixture-DSNs significantly outperforms just the mixture. Figure \ref{fig:dsn_plot}, we illustrate the learning of DSNs over epochs (comparing both the training and validation set performance) in both the mixture case (Figure \ref{fig:dsn_plot} (a)) or individual functions FL or FB (Figure \ref{fig:dsn_plot} (b) and (c)).

\begin{figure}[h]
    \centering
    \includegraphics[width=0.45\textwidth]{compare_dsn_arXiv.PNG}
    ~
        \includegraphics[width=0.45\textwidth]{query_dsn.PNG}
    \caption{\textbf{(Left Figure):} Comparison of DSN with other methods. In all methods,  submodular functions were instantiated using visual bag-of-words feature representation. *The DSN Mixture and BOW Mixture using the same components. \textbf{(Right Figure):} Performance of DSN for Query-focused Summarization on Test data.}
    \label{fig:compare_dsn}
\end{figure}

In the second set of experiments, each image is represented using visual-word features (as described in Section 5 of \cite{tschiatschek2014learning}). We do this to compare with the mixture model of \cite{tschiatschek2014learning}, which uses the BOW features instantiated with 594 components of submodular functions. In contrast, we define our DSN only with four components. The results are shown in the left figure of Figure~\ref{fig:compare_dsn}. We achieve a VROUGE score of 1.02 which is close to the 1.13 achieved using 594 components (which is also significantly more expensive for both inference and learning). We also compare against a simple mixture model using the same four components, which achieves a VROUGE score of 0.85 (BOW-Mixture in the figure). We additionally compare against DSFs~\cite{dolhansky2016deep} and DSMs~\cite{tschiatschek2018differentiable}, both instantiated with BOW features which achieve 0.78 and 0.81 respectively.

\subsection{Query-focused summarization}

We used a DSN of the form $F(A, Q, x^{(n)}, w, \theta, \lambda) = \sum_{i=1}^M w_i I_{f_i}(A, Q, x^{(n)}, \theta, \lambda_i)$. For the parameterized feature embedder, we use a hidden layer of 959 units of sigmoid activation whose weights are initialized using Xavier initialization \cite{glorot2010understanding}. The similarity kernels $S$ and $S_Q$ are computed using cosine similarity. For these set of experiments, we initialize the DSN with either GCMI (\ref{eq:GCMI}) or FL2MI (\ref{eq:FL2MI}). The mixture weights are randomly initialized, and the DSN was trained end-to-end using Adam and a decaying learning rate. We used the query-image summarization dataset~\cite{kaushal2020unified,tschiatschek2014learning}.

We instantiate the DSN's mixture components by using a concatenated probabilistic feature vector obtained from the YOLOv3 model \cite{redmon2018yolov3} pretrained on OpenImages and a scenes model \cite{zhou2014learning} pretrained on Places365 dataset. Figure \ref{fig:compare_dsn} (right figure) compares SMI functions instantiated with DSN learned features to SMI functions instantiated with the pre-trained features, and we see that the DSN functions significantly outperform the functions without joint feature learning.

\section{Conclusions}
In this paper, we presented Deep Submodular Networks, which enables us to jointly learn features along with the submodular function parameters. We study the parameter learning problem for this network using the max-margin learning framework, which enables end-end learning of both the submodular network parameters (weights and internal parameters), and the joint features. We demonstrate the utility of DSNs on both generic and query focused image-collection summarization, and show significant improvement over the state-of-the-art. In particular, we show that DSNs outperform simple mixture models using off the shelf features. Secondly, we also show that just using four submodular functions in a DSN with end-to-end learning performs comparably to the state-of-the-art mixture model with a hand-crafted set of 594 components~\cite{tschiatschek2014learning} and outperforms other methods \cite{tschiatschek2018differentiable, dolhansky2016deep} for image collection summarization.

\bibliographystyle{unsrt}

redpajama_set_name: RedPajamaArXiv


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We reported in the fall that Spotify, the Swedish streaming music platform, signed a 17-year lease for an office of 380,000 square feet at 4 World Trade Center in Manhattan, New York, at a cost of SEK 275 million a year (about $32.5 million).
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Spotify announced at the end of July that it has 60 million paying subscribers and over 140 million active users. The numbers meant the Swedish company was up 10 million subscribers since March, when it last announced it had 50 million. Since 2016, Spotify's user base has grown exponentially faster than that of its nearest competitor, Apple Music, which had around 27 million subscribers as of June 2017. Could this mean the streaming service is finally on its way to profits? In spite of an upcoming likely public listing few things point to that. In 2016, losses increased by 133 percent over the previous year to SEK5.3 billion (approximately $650 million).


text:
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Rachel Ziemba
Rachel is a geo-economic and country risk expert and provides macroeconomic and policy due diligence and develops investment scenarios.
She is an accomplished Macro strategist focused on country economic and political risk. Focus on mapping macro and policy scenarios to market outcomes/risks/mispricing. Trade and investment policy, Sanctions, Emerging Market Economies, Oil Producers, Petrodollars, Sovereign Wealth Funds, Central Bank Reserves. Country Expertise: Global EM, China, BRICS, GCC.
She is an Adjunct Senior Fellow at the Center for a New American Security (CNAS) and teaches at New York University's Center for Global Affairs. Rachel previously served as the head of emerging markets research at Roubini Global Economics, a global macro strategy and country risk firm.
She holds a bachelor's degree from the University of Chicago with honors, and a Master of Philosophy degree in international relations with a specialization in international political economy from St. Antony's College, Oxford University (with distinction).
Jeffrey Christiansen
Jeff has over a decade of experience in risk and economic affairs having served in varied roles in both the private and public sector in the USA and Middle East.
Jeff has built risk advisory and research credentials over many industries spanning research, intelligence, finance, and consulting, he has worked with Bain & Company, Moody's Investor's Service, AfricInvest Capital Partners, the US Department of State, and the Carnegie Endowment for International Peace.
He is expert in sovereign risk analysis, focusing on African and Middle Eastern governments and development banks. He spent several years as a management consulting with Bain, focusing on corporate strategy, PMO, organizational change, and policy implementation. Prior to grad school, Jeff taught courses and workshops on negotiation strategy, developing proficiency in French and Arabic on the side.
Jeff has a Masters at the School of Foreign Service at Georgetown University.
James Clifford
James is an expert in liability structure optimization for corporates and was previously Head of Natural Resources Financing, EMEA Leveraged Finance at Barclays in London.
Previously he was Head of Natural Resources Financing, EMEA Leveraged Finance at Barclays covering non-investment grade corporates. Over the course of his career James has led more than $30bn of financing transactions across a wide range of industries and geographies.
His extensive product experience spans lead loan, bond and mezzanine underwritten financings for large caps and private equity to mid-cap club loan transactions. James has a proven track record of quality of advice given to management and private equity on capital structuring and financing strategy.
James studied Psychology at Oxford University and Management and Finance at Cambridge University.
Moritz Kraemer
Moritz is the Chief Economic Advisor of Acreditus an experienced economist, credit analyst and commentator with 20+ years of market experience.
He has a special focus on sovereigns globally (both EM and advanced) and multilateral development banks. Worked in 100+ countries. Very extensive publication, speaking and media footprint on macroeconomic and credit issues. A demonstrated history as a thought leader and communicator in Credit Analysis, Macroeconomics, Bonds, and Capital Markets.
Moritz was previously the Global Chief Rating Officer Sovereign Ratings for Standard & Poor's ratings where he coordinated global analytics, research and market outreach.
Prasant Sarkar
Prasant is a veteran GCC banker and a subject matter expert in corporate governance, enterprise risk management, compliance and ethical banking
He specialises in corporate governance and enterprise risk management. He has been instrumental in designing, establishing and driving full fledged risk management functions in a number of banks in various Middle Eastern countries. He has also been associated in teaching in various Universities and Banking Institutes notably with Bahrain Institute of Banking & Finance.
Prasant also acts as a mentor, coach and evangelist to budding entrepreneurs and start up projects in addition to his current roles. Previously he was the Chief Risk Officer in National Bank of Fujairah and has over four decades of experience in the banking and financial services industry
Saber Hussain
Saber is a career Investment Banker with over 22 years' experience and recognized expertise in Global Capital Markets, Securitization and Liquidity and Funding
Saber's career has spanned across multiple banking disciplines and his areas of financial expertise include: Capital Markets-(primary and secondary markets) syndication, Securitization structuring and Funding, Liquidity and Funding, Collateral Optimisation, Risk and Regulatory, Structured Bonds and ABS structuring, fund-linked and asset/ liability multi-asset class derivatives solutions.
Previously he was COO for Global Credit Trading at Barclays Investment bank in London and Dubai. Saber holds a Bachelor's and Master's Degrees in Mathematics from King's College London and a Master's in Business from City University Business School.
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"Lunch was the first thing I cut back on," says Ms. Snider, who works as an assistant account executive for a public-relations firm in Atlanta.
Make room for another victim of the weak economy: lunch.
Ms. Snider is part of a growing tide of midday restaurant regulars who are changing their habits to save cash. Instead of indulging in a roasted portobello sandwich and gourmet chips at a nearby bistro, many workers are now brown-bagging it. In fact, online bag retailer eBags.com saw sales of lunch bags and coolers increase 39% in June over the year before. Other workers are turning to cheaper alternatives -- like subsidized or free corporate cafeterias and cheaper fast-food spots.
It's not just the young, entry-level workers who are cutting back. In March, after price increases of 10% to 20% at his favorite midtown Manhattan eateries, Marc Haskell gave up his gourmet spinach salads and turkey wraps and began packing turkey sandwiches from home. The 47-year-old executive vice president of the Glazier Group, a restaurant and hospitality company, says he now saves around $50 a week on lunch.
Though he occasionally misses getting out of the office and socializing with co-workers during lunch, he says eating at his desk has made him more productive. "Instead of going out for an hour, walking to the place and waiting on line, I can basically eat in 10 to 15 minutes and catch up on some news."
Popular lunch-hour restaurants say increases in both wholesale food prices and vendors' fuel surcharges are putting a squeeze on their profits. Indeed, the consumer-price index for all food has risen 5.1% since May 2007 and is expected to rise even further. Then, after price jumps, come the cutbacks from consumers, which further hurt restaurants. According to a Zogby International survey of 755 people conducted in May for Marlin Co., a workplace communication firm, nearly half of employees surveyed had reduced their food purchases.
As a result, restaurateurs cite a decrease in lunchtime traffic -- and say existing customers now gravitate toward lower-price items on the menu.
Some are trying to adapt: In February, Costello Sandwich & Sides on the North Side of Chicago, for example, added a less-costly half-sandwich option to its menu in response to customer demand, says Chris Costello, the owner, who adds that this will be the first "no-growth year" in the two-location restaurant's 10-year history.
Lisa Hall, owner of Kitchenette, two home-style eateries in New York City, recently added an incentive for frequent patrons: For every 10 sandwiches a customer buys, they get the next one free. The goal is to boost revenue with additional sales. "We can charge an extra 25 to 50 cents, but that doesn't even halfway cover the extra costs we are being charged," she says.
Nick Liuzzi, owner of Samantha's Trattoria in New York City's Battery Park, typically caters to brokers and banker-types. But, as those industries suffer, Mr. Liuzzi estimates that the Italian delicatessen has seen a 15% to 20% drop in lunchtime traffic since last year. "People are more conscientious of their spending; even corporate spending is down," he says.
Some former $50-a-week lunch patrons haven't fully abandoned lunches out, but have instead traded in table service for standing in line for a cheaper kind of sandwich. Subway Restaurants says its $5 footlong sandwiches are luring the lunch set away from pricier restaurants. A spokesman for the chain says there has been an increase in customer count since implementing the $5 footlong nationally in late March.
Jennifer Nichols, an account executive for a San Francisco public-relations firm, is one of those new customers. She used to spend $10 a day on gourmet salads and ham-and-cheese sandwiches. About four months ago, the restaurants started to eliminate the cheaper combos meals, and the cost of her lunch became noticeably high. So Ms. Nichols headed to Subway. Now, she buys a footlong sandwich for $5 and saves the second half of it for the next day's lunch. To further cut costs, Ms. Nichols now buys her drinks and chips at supermarkets or convenience stores. Her new total tab for lunch: $15 to $20 a week.
Some employees are discovering that there is such a thing as a free -- or subsidized -- lunch.
Lawrence Minicone, a financial consultant with FactSet Research Systems Inc., says that lately he's been heading to the company cafeteria. FactSet, a financial-data provider based in Norwalk, Conn., has provided free lunch four days a week to its 1,800 employees world-wide for years. However, in the past few months there has been a 10% to 15% increase in employees frequenting the cafeteria.
"Because of the rise in food costs, FactSet's policy really helps," Mr. Minicone says. In some cases, employers who didn't offer much in the way of lunch fare are stepping in to alleviate rising food costs.
At public-relations firm Weber Shandwick's Dallas office, management has extended free lunches from the first Monday of each month to six or seven times a month since January. Ken Luce, president of Weber Shandwick California and Southwest, says about 80% of the firm's 102 employees take advantage of the lunches, up about 10% to 15% since last year.
Mr. Luce finds that when the food is brought in, employees eat as a group, which encourages collaboration and boosts morale. He adds that while it may cost more to provide lunch, which ranges from $7 to $12 per person, it makes employees happy. The meals also offset "the cost of turnover," he says, because it's cheaper to buy lunch and retain valued employees than it is to hire and train new workers.
GotVMail Communications, a telecommunications company based in Needham, Mass., started subsidizing employee lunches last summer to offset rising food and gas prices in the area. The company's 48 employees now get a $5 food credit each day that offsets the cost of lunch at area vendors. David Hauser, GotVMail's co-founder and chief technology officer, says it works out to an added benefit of $1,150 per year per employee, plus gas savings for employees who no longer have to drive to eateries since food is now delivered. About half of GotVMail's employees take advantage of the program.
Mr. Hauser says it's been great to watch friendships form in the lunchroom between people from different departments who hadn't previously spoken. And, he says, it's common to find employees chatting about work projects over the company-subsidized lunch.
Employers stand to benefit from providing employees lunch. "It could boost productivity in that there's less travel time and less transition time," says Julie Morgenstern, a workplace productivity consultant based in New York and author of "When Organizing Isn't Enough." Still, she says employers should encourage workers to get fresh air or leave the building for a few minutes each day so that they don't make a potential productivity boost a "productivity sink."
Link to this article on WSJ Online: "Bagging Lunch: The Inflation Effect"


A CPU family from Intel designed to supersede Intel's x86-based servers. Although an advanced hardware architecture, and even with HP as its major supporter, Itanium gained only a fraction of the server market dominated by the x86 line. In addition, the compilers necessary to take full advantage of Itanium's elaborate architecture were never fully developed. By the time Itanium gained ground in the early 2000s, there were too many x86 servers running worldwide, and x86 performance was improving.
HP-UX (HP's Unix) and several other Unix versions run on Itanium; however, in the 2010 time frame, the Itanium versions of Windows Server, Red Hat Linux and Ubuntu Linux were given end of life.
After 64-bit Itanium chips were introduced in 2001, Intel upgraded its x86 CPUs to 64 bits, and over the years added advanced security and fault detection features into high-end x86 Xeon chips. See Intel 64 and Xeon.
Itaniums run native applications and emulate x86 and HP PA-RISC apps. x86 programs are executed in hardware or in software (see IA-32 Execution Layer). HP PA-RISC apps are translated in software (see Aries). For more on the Itanium architecture, see IA-64.


At the end of the day, everybody shares a similar goal in online business- to profit. As you've learned, creating and maintaining a blog is a lot of work and has the potential to be highly rewarding if done right. It is important to remember that blogging is not a get rich quick scheme, but is rather a method for earning a long-term, passive income. With hard work and the use of the strategies you've learned throughout this course, you will be able to enjoy life as a Top Blogger while making good money!


There are three chapters in this dissertation. In the first two chapters, I focus on the deceleration of college wage premium growth at national level. In the first chapter, I use a decomposition and counterfactual simulation approach to evaluate three explanations of flattening college wage premium during 2000-2010 and 2000-2015, i.e., demand reversal, polarization and supply change. The simulation results suggest: i. supply change is the most powerful explanation; ii. polarization has expla-natory power but not robust; and iii. demand reversal has little eÿcacy in explaining the fact. By a close look of three explanations, I argue that the "failure" of demand reversal story and the "unstable" impact of polarization story indeed suggest some kinds of weakness of the analytical framework. And there are still some unexplained facts related to the supply change story. A better framework should depend on the endogenous supply of high-skilled workers and heterogenous quality of high-skilled workers. Due to the weakness of the analytical framework in the first chapter, in the second chapter, I use a di˙erent framework to study the deceleration of college wage premium growth. The novel feature of the framework is that the supply of high-skilled workers endogenously depends on the progress of technologies and other shocks. I show the change in progress basis of skill-biased technologies is the primary reason that leads to the deceleration relative to the change in progress speed of skill-biased technologies and the progress of automation. This result suggests the deceleration of college wage premium growth is an inevitable outcome of skill-biased technological change while both demand reversal and polarization have only moderate explanatory power. About the direct mechanism of the deceleration, I find that the faster decline in the mean quality of high-skilled workers followed by a greater o˙set of skill-biased technological change's positive impact on college wage premium growth due to increase in the progress basis of skill-biased technologies is the primary direct mechanism. Unlike the first two chapters, the third chapter looks into sub-national level labor market inequality change. This chapter studies a spatial pattern and a possible channel of local labor market inequality change. That is, large cities have the greater losses in the declining industries and greater gains in the growing industries. When the declining and growing industries are low-skilled and high-skilled intensive respectively, and the growing industries are skill intensive, the losses and gains of industries lead to a greater increase in the local labor market inequality of those first larger cities. The empirical results show that, one standard deviation change in initial city size accounts for 71.3% to 80.5% of one standard deviation change in industrial composition where the industrial composition change refers the losses in manufacturings (NAICS 31-33, low-skilled worker intensive) and gains in professional services (NAICS 52, 54, and 62, high-skilled worker intensive) here. One standard deviation change in the industrial composition change accounts for 79.5% to 89.8% of one standard deviation change in the local labor market inequality. And overall, the one standard deviation change in initial city size accounts for 62.1% to 65.1% of one standard deviation change in the local labor market via the industrial composition change channel. These empirical results verify the spatial pattern and the channel. And the paper compliments many related discussions in various ways.
Huang, Yiheng, "Essays on Skill-Biased Technological Change and Labor Market Inequality" (2018). All Dissertations. 2140.


text:
History of Memorial Day
I performed a great deal of research on the history of Memorial Day in order to provide you with accruate information. However, the website www.usmemorialday.org did such a great job, there was no need for me to say anything more!
[Source: www.usmemorialday.org]
Memorial Day, originally called Decoration Day, is a day of remembrance for those who have died in our nation's service. There are many stories as to its actual beginnings, with over two dozen cities and towns laying claim to being the birthplace of Memorial Day. There is also evidence that organized women's groups in the South were decorating graves before the end of the Civil War: a hymn published in 1867, "Kneel Where Our Loves are Sleeping" by Nella L. Sweet carried the dedication "To The Ladies of the South who are Decorating the Graves of the Confederate Dead" (Source: Duke University's Historic American Sheet Music, 1850-1920).
While Waterloo, N.Y. was officially declared the birthplace of Memorial Day by President Lyndon Johnson in May 1966, it's difficult to prove conclusively the origins of the day. It is more likely that it had many separate beginnings; each of those towns and every planned or spontaneous gathering of people to honor the war dead in the 1860's tapped into the general human need to honor our dead, each contributed honorably to the growing movement that culminated in Gen Logan giving his official proclamation in 1868. It is not important who was the very first, what is important is that Memorial Day was established. Memorial Day is not about division. It is about reconciliation; it is about coming together to honor those who gave their all.
History of Memorial Day -- General John Logan
Memorial Day was officially proclaimed on 5 May 1868 by General John Logan, national commander of the Grand Army of the Republic, in his General Order No. 11, and was first observed on 30 May 1868, when flowers were placed on the graves of Union and Confederate soldiers at Arlington National Cemetery. The first state to officially recognize the holiday was New York in 1873. By 1890, it was recognized by all of the northern states. The south refused to acknowledge the day, honoring their dead on separate days until after Word War I (when the holiday changed from honoring just those who died fighting in the Civil War to honoring Americans who died fighting in any war). It is now celebrated in almost every State on the last Monday in May (passed by Congress with the National Holiday Act of 1971 (P.L. 90 – 363) to ensure a three day weekend for Federal holidays), though several southern states have an additional separate day for honoring the Confederate war dead: January 19 in Texas; April 26 in Alabama, Florida, Georgia, and Mississippi; May 10 in South Carolina; and June 3 (Jefferson Davis' birthday) in Louisiana and Tennessee.
History of Memorial Day -- Poppies
In 1915, inspired by the poem "In Flanders Fields," Moina Michael replied with her own poem:
We cherish too, the Poppy red
That grows on fields where valor led,
That blood of heroes never dies.
She then conceived of an idea to wear red poppies on Memorial Day in honor of those who died serving the nation during war. She was the first to wear one, and sold poppies to her friends and co-workers with the money going to benefit servicemen in need. Later a Madam Guerin from France was visiting the United States and learned of this new custom started by Ms. Michael and when she returned to France, made artificial red poppies to raise money for war orphaned children and widowed women. This tradition spread to other countries. In 1921, the Franco-American Children's League sold poppies nationally to benefit war orphans of France and Belgium. The League disbanded a year later and Madam Guerin approached the VFW for help. Shortly before Memorial Day in 1922 the VFW became the first veterans' organization to nationally sell poppies. Two years later their "Buddy" Poppy program was selling artificial poppies made by disabled veterans. In 1948 the U.S. Post Office honored Ms. Michael for her role in founding the National Poppy Movement by issuing a red 3 cent postage stamp with her likeness on it.
History of Memorial Day -- Traditional Observance
Traditional observance of Memorial Day has diminished over the years. Many Americans nowadays have forgotten the meaning and traditions of Memorial Day. At many cemeteries, the graves of the fallen are increasingly ignored, neglected. Most people no longer remember the proper flag etiquette for the day. While there are towns and cities that still hold Memorial Day parades, many have not held a parade in decades. Some people think the day is for honoring any and all dead, and not just those fallen in the service to our country.
There are a few notable exceptions. Since the late 50's on the Thursday before Memorial Day, the 1,200 soldiers of the 3d U.S. Infantry place small American flags at each of the more than 250,000 gravestones at Arlington National Cemetery. They then patrol 24 hours a day during the weekend to ensure that each flag remains standing. In 1951, the Boy Scouts and Cub Scouts of St. Louis began placing flags on the 150,000 graves at Jefferson Barracks National Cemetery as an annual Good Turn, a practice that continues to this day. More recently, beginning in 1998, on the Saturday before the observed day for Memorial Day, the Boy Scouts and Girl Scouts place a candle at each of approximately 15,300 grave sites of soldiers buried at Fredericksburg and Spotsylvania National Military Park on Marye's Heights (the Luminaria Program). And in 2004, Washington D.C. held its first Memorial Day parade in over 60 years.
History of Memorial Day -- Moment of Remembrance
To help re-educate and remind Americans of the true meaning of Memorial Day, the "National Moment of Remembrance" resolution was passed on Dec 2000 which asks that a 3 p.m. local time, for all Americans "To voluntarily and informally observe in their own way a moment of remembrance and respect, pausing from whatever they are doing for a moment of silence or listening to Taps."
The Moment of Remembrance is a step in the right direction to returning the meaning back to the day. What is needed is a full return to the original day of observance. Set aside one day out of the year for the nation to get together to remember, reflect and honor those who have given their all in service to their country.
History of Memorial Day -- More Information
But what may be needed to return the solemn, and even sacred, spirit back to Memorial Day is for a return to its traditional day of observance. Many feel that when Congress made the day into a three-day weekend with the National Holiday Act of 1971, it made it all the easier for people to be distracted from the spirit and meaning of the day. As the VFW stated in its 2002 Memorial Day Address: "Changing the date merely created three-day weekends and has undermined the very meaning of the day. No doubt, this has contributed greatly to the general public's nonchalant observance of Memorial Day."
On January 19, 1999 Senator Inouye introduced bill S189 to the Senate which proposes to restore the traditional day of observance of Memorial Day back to May 30th instead of "the last Monday in May." On April 19, 1999 Representative Gibbons introduced the bill to the House (H.R. 1474). The bills were referred to the Committee on the Judiciary and the Committee on Government Reform.
To date, there has been no further developments on the bill.
For more information on the history of Memorial Day, go to www.usmemorialday.org
Shelly's comments: Please read and pass along this History of Memorial Day and take time to remember those who gave everything so we could be free.
As part of this history of Memorial Day, please remember to support and pray for our military and allies by Supporting the Troops past and present on Memorial Day and every day thereafter.
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It is 40 degrees, it is 10 degrees - it is warm and sunny and yet it is still winter. We're still peeling the wing covers off the plane and plugging in the engine heater. We are flying more as Spring approaches even though it is still March in the mountains.
And while the weather ebbs and flows, so does our energy policy - going green with more wind and solar energy - and quickly back to 'drill baby drill'. Oil and gas and oil shale - unrest in the Middle East - higher gas prices and yes, right back to 'drill baby drill'. Who says mid-term elections don't matter.
It was one of those aforementioned transition days where it was warm in the valleys and winter in the mountains. We made our way from Vail-Eagle in Colorado to the Piceance Basin (geologic oil-rich basin in north-west Colorado that is part of the Green River Basin formation) to attend oil shale meetings and briefings and give our passenger experts the birds' eye view of oil-shale leasing country and the impacts this will have on our water resources and large scale landscapes.
For those unfamiliar to the Piceance Basin, it has provided critical habitat and migration corridors for elk and mule deer populations and clear streams for native cutthroat trout.
A round of oil-shale leasing is working its way through the U.S. Bureau of Land Management to allow energy companies to test and develop oil shale technologies in the Basin. Oil shale development requires an incredible amount of energy because large amounts of rock must be heated to extremely high temperatures - up to 900 degrees - to release the oil.
Experts from D.C. and local activists were getting their first aerial look at current drilling activities and potential leasing sites. The flights put into perspective the vast areas involved, the pristine lands that would be compromised, and encouraged debate on where our future energy might come from in the USA. The discussion revolved around the topics of climate change, whether this technology is feasible, and the rampant use of our precious water resources.
There is much activity on Capitol Hill and in the West regarding oil shale. A 2010 Government Accountability report projects that the water usage demands of oil-shale development - hundreds of thousands of acre-feet per year - greatly exceed water availability in the Colorado River Basin.
And of course my own personal opinion re climate change is that we as a people take out insurance on absolutely everything from our cars to our pets, so why wouldn't we hedge our bets on the possibility that climate change exists and insure against any development that might contribute to this.
Recent polls show that an overwhelming majority of people in western States believe that protecting our water should be a priority and more than two thirds of respondents felt that we can have a strong economy and protect our air, land and water.
Balanced energy plans create jobs.
From sun to snow to changing policies and politics...welcome to our world.


I'm not trying to be rude or anything, but could you include more punctuation into your posts? It makes it really hard to read when there are no periods.
That's okay, you're still doing pretty good.
As for the main post, I always got the impression that life for Keyblade wielders was pretty boring up until TAV's era. MX, ME, and Yen Sid were pretty much the semi trio of their time so I don't think there was a staple female. Just dudes broing it up.
On top of there being no major danger at the time, like TAV, I don't think YMX and YME were really supposed to leave the world much. ME probably didn't mind it much, but MX certainly felt stagnated, which I think is the more likely reason as to why he was passed up as Successor. MX likes to travel and not be tied down to one place for too long. In order to be Successor one must permanently stay at LoD to watch over it. I think MX would hate a job like that, lol.
One has to remember that there was no dangers until Xehanort went rouge. Before that I imagine they had a boring life over all but a cool one given they could travel anywhere they want literally.
As for a story on YX or a young ME....meehhhh honestly I'm okay doing without. I'm rather tired of Xehanort.
Though I have a feeling we'll be getting one for the prologue of KH3 =p.
Until YX went rogue I think the most interesting part of them we'll see is how they both came to become wielders. Eraqus could easily had gone through the succession ceremony or been chosen due to being worthy. And Xehanort would be the more interesting since he was just a boy on an island, his curiosity would have to lead him to something. Perhaps the same door our young adventurers have run into?
KH3 I don't mind given the discussion trailer. Beyond that though, no.
I'd say YX's reason for leaving DI was simple curiosity. It's shown in some reports he was like Riku and found the islands a prison and stifling. So to have a keyblade master appear and offer him apprenticeship and freedom would likely and honestly be all the motivation YX needs to leave.
I highly doubt he used a raft. It's likely that once a keyblade chose him he found his way to his master or that even the master he had found him after the fact and took him with him.
In the end YX wouldn't have been able to leave on his own. Even with the keyblade you can't travel unless you know how to work it.


text:
Irish Films
Archives For Irish Films
There is something about the Irish.
Their accents.
Their humor,
or humour.
Their music.
And oh their movies!
What I love about the Irish-set or Irish made films is that they have a soul that very few other movies are able to capture. You can sense it in how they present their ideas throgh film–there is an honesty about life and it does not seem forced but often has a whimsical nature, which is inspiring.
When you think about Irish culture and its history, you may recall St. Patrick, Celtic music, marvelous green landscapes, war, the Potato Famine, immigration, lots of drinking, and the 20th Century fighting known as "The Troubles."
Despite a fairly rough history, the Irish always seem to be optimistic, joyful, and occasionally magical. It shows in their films, which is why I have loved so many of them. I've learned that you don't have to be Irish to love these films. You just need to have a soul that is open to laughing and growing.
I have had this blog in a draft form for over a year but after recently watching Brooklyn, it kicked me to finish it.
Here are five of the most impactful Irish movies as well as a few others I recommend.
Waking Ned Devine (1998)
When a lottery winner dies of shock, his fellow townsfolk attempt to claim the money. And yes, this is a comedy.
If you want to get a sense of the dark humor of the Irish, you'll love this movie. A word of warning, there is crazy old man nudity in a race to cover up one of the funniest movie plots in years.
If I were to name my favorite Irish movie, it would be this. Follow along some down and outers from North Dublin who try to make is as a "soul" band. It has some of the foulest language but the finest of Irish-accented deliveries. Every year I put the soundtrack on the stereo to hear the Irish soul of Otis Redding and Wilson Pickett.
I've written about this before as I still believe it is one of the most beautiful and relevant depictions of the Gospel (other than the actual Gospel) ever on film. Enough said.
Here you see a grown up Glen Hansard (from his teenage years with The Commitments). He plays an unnamed Irish street musician in Dublin trying to get by. He meets a young female immigrant from Eastern Europe who shares his loves for music. You see their love of music and longing for connection blossom in the movie. And again, the soundtrack is golden.
What a delightful movie about courage, family, community, friendship, loneliness, and love. The movie thrives because of the subtle wins for the main character played by Saoirse Ronan and feels more like real life than the over the top dramatic. One of my favorite writers, Nick Hornby (Fever Pitch, High Fidelity, About a Boy), wrote the screenplay.
Other great Irish films I recommend:
71′ – not technically Irish but set in Northern Ireland
Bloody Sunday
The Wind That Shakes the Barley
My Left Foot
I did not see Angela's Ashes so please don't hit me.
And before anyone brings it up…P.S. I Love You and Patriot Games don't quite qualify.
In Culture, Movies	71', Best Irish Movies, Bloody Sunday, Brooklyn movie, Calvary movie, Evelyn, films, Films about the Gospel, In the Name of the Father, Ireland, Irish Films, Irish movies, Michael Collins, Movies, My Left Foot, Once, Sunday Bloody Sunday, The Boxer, The Commitments, The Irish, The Troubles, The Wind That Shakes the Barley, Top Irish Films, Top Irish Movies, Waking Ned Devine
This week has left me a bit unsettled in terms of movies. The Oscars came and went I just sighed, "oh well."
I am usually pretty spot on with movie award nominations (and winners) but this year has been all over the place with no single film standing out for the masses.
As there are so many good books that get overlooked, there are also so many good movies who suffer the same anonymity, especially in the flare of blockbusters.
One that sticks out is the 2014 film , Calvary, starring Brendan Gleeson (remember Hamish from Braveheart?), Chris O'Dowd (Bridesmaids), and Kelly Reilly (Flight). It is an Irish-made film by the talented director, John Michael McDonagh. I watched it in theater last year and then again this past week at home. I have always had an affinity toward Irish films like The Commitments, Waking Ned Devine, Michael Connolly, In the Name of the Father, Bloody Sunday, and Once. There is something about the unique dry humor in Irish films as well as their ability to hit some of the deepest emotional themes in life through storytelling.
Calvary may just be one of the finest films that has ever moved me. My film aficionado friend Erik Parks featured Calvary as the top movie of 2014, even beating out some of the Oscar winners this year. I agree with him.
The Irish sure know how to tell a story, especially a familiar story.
Erik shared a great overview of the movie from his blog:
The opening of this film shows a good priest in confession as he listens to a mystery man recount his years of sexual abuse by a bad priest. He then tells the good priest that as an act of revenge, he plans on killing him in a week. Crazy setup but a fantastic film that shows a faithful man of God dealing with psychological torture as he continues to love and care for the wicked people of his town. Mercy, love, forgiveness and ultimately Christ-likeness are the overarching themes in this dark, but extraordinary little Irish film. (Rated R for sexual references, language, brief strong violence and some drug use.)
I'm not sure why people are so afraid of the Gospel. When you read about Jesus, this is what we get. It's very R-rated. It's bloody. It's violent. It's poignant and challenging. As you read in the Bible within the books of Matthew, Mark, Luke, and John, you get the nitty-gritty of Jesus' life and death and in Calvary the movie, it follows well. That is why Calvary succeeds because it doesn't hold back from real life.
Calvary may be the best movie representation of the Gospel I've ever seen.
It is rich with symbolism as well as lines to make you contemplate and pray over.
Father James Lavelle: "God is great and the limits of his mercy have not been set."
Father James Lavelle: I think there's too much talk about sins to be honest not enough talk about virtues.
Fiona Lavelle: What would be your number one?
Father James Lavelle: I think forgiveness has been highly underrated.
Father James Lavelle: He was a good man, your husband? (to a lady who just lost her husband)
Teresa: Yes. He was a good man. We had a very good life together. We loved each other very much. And now… he has gone. And that is not unfair. That is just what happened. But many people don't live good lives. They don't feel love. That is why it's unfair. I feel sorry for them.
Your life will be changed after watching it. My encouragement is to go see for yourself and let me know what you think.
In Faith, Movies	Brendan Gleeson, Calvary movie, Chris O'Dowd, grace, Great Movies, Irish Films, Irish movies, John Michael McDonagh, Kelly Reilly, movies about grace, Movies about Sacrifice, oscar worthy movies


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