I think it's disgusting that your free version of ccleaner now installs avast antivrus without my knowledge.
No check box, no warning.
I also read on the avast forum that it is meant to show a pop up box.
I even installed it twice to see if I get this pop up. Which I didn't.
I wont be future updating ccleaner anymore due to the installing software without my knowledge.
Now I have got to waste 20 minutes of my own time fully deleting this surplus antivirus crap off my computer.
The irony of a cleaner program, installing junk on my computer.
The opt-out is found on the first page of the installer.
It is! But if you had installed Avast already; the Avast offer won't show up again.
I have never installed Avast knowingly, I have never seen that message ever.
And when ccleaner installed it, I completely uninstalled it with revo uninstaller, then I ran the avast remover tool. Then ccleaner, and then 2 registry cleaners.
Still no avast message on install.
So I would like to know where exactly this is referencing to show this message whether it be a cookie, registry or folder.
Because I assure you it doesn't show on my computer. And yes I have worked in IT all my lfe, so you can be as technical as you like in your answer.
Maybe it shows on a brand new computer that has never had a copy of ccleaner installed ever.
But considering people were complaining about this on the avast forum a few months ago, it obviously hasn't been tested properly.
" Where appropriate, CCleaner Free may recommend improvements to your system's antivirus protection "
I joked with the last version that the decline of CCleaner had started, it's spiralling ever quicker by the looks of it.
That's why I'm going to hold off updating immediately anymore. I'll just wait and see if there's any fallout with any new versions. And I definitely don't require any recommendations for "antivirus improvements".
When I installed this latest update, it was on the very first install screen, in the lower left hand corner.
I checked the registry and found the following key "HKEY_CURRENT_USER\Software\AvastAdSDK\". If I delete the value "LastOffer" the Avast offer shows up again.
I didn't receive any opt out. Nothing was on the install screen or anywhere else mentioning Avast. After I updated CCleaner to the newest build I had an Avast Icon on my desktop. I understand it does show up for some people but not for everyone obviously. That's a serious issue that needs to be addressed not just because of the obvious reason but also installing it can cause some major issues if a person already has active protection on their PC.
I made an account just now on these forums because it is indeed that serious of an issue. Piriform needs to fix this issue ASAP or remove Avast from CCleaner.
Piriform needs to fix this issue ASAP or remove Avast from CCleaner.
I doubt they'd remove it since Avast owns them and is likely calling the shots.
- "Automatically clear check boxes to install additional software & warn of attempts to install additional progs"
- "Don't show installation steps that may contain adverts & offers for additional apps"
Is it possible you have something similar that didn't fully work with the newest version of the CCleaner installer?
What does it mean? I have prohibited the collection of information!!!
I've been using CCleaner for years, and now it installs Avast WITHOUT ANY NOTICE. Suddenly an orange icon appeared and now I need to reboot my PC to finish Avast uninstallation. Absolutely unacceptable. Signed up into the forums to inform that now you have one less customer.
Why do you need to resort to this type of sneaky unethical promotion in the first place? Why not make it opt-in instead. Are you that desperate for revenue?
I had no idea you were cramming Avast down my throat. There was NO WARNING. I already tried this product and it caused a conflict. I just wasted 3 hours getting rid of a crappy product that I didn't know was there and that you forced on me. I can't believe a disk cleanup product would install malware on my machine. You have completely violated the trust that was placed with you. Even worse, you engage in public arguments with your customers to try to justify your misdeed.
I'll be getting a refund from you. Only I'll be a douche bag just like you were. Instead of politely asking you for a refund I'll contest the charge with my credit card company and force a chargeback fee on you.
Yeah, they are hiding the title page that has the Avast Installation option.
The installer does not offer Avast (or Chrome) for every installation, only occasionally. If the first page is empty, nothing additional will be installed.
I joined this forum to explore this new problem with ccleaner adding avast to its latest updates.
I have been using "CCleaner - Portable" found here (https://www.ccleaner.com/ccleaner/builds) if you want to download it on ccleaner.com.
I find that it uses less ram and space on my disk, plus does not clutter up my dll registry. When it is time to update I come over to the link above and get the latest portable version and install it.
I found out about this new problem with ccleaner on another forum that stays on top of the latest software updates and suggests to its members to install or to avoid the new updates.
....If you google search " problem with ccleaner installing avast " without quotes, you will see how many sites and users are deeply troubled and suggesting others not install the latest updates.
Someone in admin on this forum should send the word/news up to the top of your company that you have a problem growing exponentially with your user base that is really ticked off.
One answer to this problem if .piriform.com does not correct this problem is anyone can go to one of the several software download sites and get an older version of ccleaner before piriform.com added their own crap (avast) to this great free software. My problem is I need someone who knows what was the last version of ccleaner that did not include avast. Maybe some user that knows from experience can post it on this thread.
---->My question is, does the portable version now have the avast install hidden in it as well? ...Can a moderator or someone who has installed the portable version tell me if avast is automatically installed when I first use it to clean my computer?
*Thank you forum members and moderators for listening to my concerns. Sorry if I sound like I am ranting, but I have loved ccleaner since I first found it many years ago and hate to see a such a great software used by millions corrupted by corporate greed. There are a lot of poor users who can not afford to buy the pro version, like elder people on fixed incomes like myself.
what was the last version of ccleaner that did not include avast.
CCleaner v5.32.6129 is the last version without any influence from the owner Avast. But old versions will have two bugs in them that can damage Windows 10 in particular the cleaners for Windows.old and Fonts Cache should be disabled/unticked in old versions and/or exclusions put in place so those files can never be deleted.
I only use the portable ZIP version and it has never tried to install anything else not even before I started blocking CCleaner .EXE files Internet access via Windows Firewall. The portable version has never installed Avast and never any Google stuff, etc., reason being the installer versions are what's needed to install the additional unwanted software.
Thank you Andavari for this info.
This solves the problem for me.
I am very glad this forum exists for users of this great software.


text:
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New Predator film will "reinvent the franchise"
Adventurer's Diary 2: The growth of Neku Sakuraba
Xbox Podcast: Peter Moore and diversity in the gaming industry
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Twitch takes Cooking Mama too seriously and now has an official cooking channel
After the resounding success of Bob Ross' The Joy of Painting series, Twitch has decided that the world needs more non-gaming streams. Thankfully, their next non-gaming stream will be a Cooking channel. Titled 'Food', the stream will feature cooking shows and ... Read More...
Fan-remake of Daredevil trailer in Fallout displays "Punishing" accuracy
Netflix's popularized take on Marvel's Daredevil has an old trailer remade in a new way. Using the audio from the official trailer, one Yoububer has recreated the trailer in Fallout 4. Thanks to the ease in mo... Read More...
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A bit of the emo Kylo Ren shows in Adam Driver's SNL promos
With the Star Wars: The Force Awakens actor set to host this week's coming Saturday Night Live, the promos have hit and a bit of Adam Driver's emotional Kylo Ren side shows through. "They brought us here to th... Read More...
Friends cast reunion will fill the blank space in your heart
The Friends cast will be reuniting on NBC this year. As a two hour tribute to James Burrow, the cast will hopefully be in the same room at the same time. NBC Chief Robert Greenblatt told reporters, "I'm hoping... Read More...
Netflix has hidden genres
Netflix has almost single-handedly changed the way people watch movies, television shows, and the way we "chill" at home. Often times when we find the strength to move on, the streaming service recommends something to us and it makes us feel like it was made j... Read More...
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redpajama_set_name: RedPajamaCommonCrawl


text:
12 Best Inspirational Drama Movies Like The Help
The Help is a period drama film directed and written by Tate Taylor and released in 2011. It is an inspiring adaptation of Kathryn Stockett's novel by the same name. It stars Emma Stone as Eugenia "Skeeter" Phelan, Viola Davis as Aibileen Clark, and Octavia Spencer as Minny Jackson. All of whom give incredible performances that pack an emotional punch. In this period drama film, Phelan is an aspiring author who turns to two highly experienced African-American maids for insight into their life experiences with white families.
Also Read: Movies Like Parasite
For those looking for other movies like The Help, several depict the era faithfully and are full of captivating narratives. To help you find the perfect film, we've compiled a list of the 12 best movies like The Help. Whether you're in the mood for a lighthearted comedy or a thought-provoking drama, there's something on this list for everyone.
1 12 Best Inspirational Drama Movies Like The Help
1.1 The Kids Are All Right
1.2 Shéhérazade
1.3 The Boy in the Striped Pyjamas
1.4 Win-Win
1.5 Still Alice
1.6 The Visitor
1.7 Pride
1.8 Sunshine Cleaning
1.9 Jane Eyre
1.10 Lion
1.11 The Way
1.12 Room
Director: Lisa Cholodenko
Writers: Lisa Cholodenko, Stuart Blumberg
Stars: Annette Bening, Julianne Moore, Mark Ruffalo
The 2010 film The Kids Are All Right follows the story of two same-sex couples, Jules and Nic, raising their teenage children together. As the children get older, they become curious about their biological father and set out to find him. This leads to a meeting with Paul, who is initially hesitant to get involved with the family. However, he eventually forms a bond with the children and becomes a part of their lives.
It is a heartwarming story about family, love, and acceptance. It's a must-see for anyone who is looking for a feel-good movie with a great plot.
Director: Jean-Bernard Marlin
Writers: Jean-Bernard Marlin, Catherine Paillé
Stars: Dylan Robert, Kenza Fortas, Idir Azougli
It is a French drama film released in 2018. It tells the story of a young woman named Shéhérazade, who is sentenced to death after being accused of a crime she didn't commit. While in prison, she befriends a fellow inmate named Safia, and together they hatch a plan to escape. The film follows their struggle to survive in the harsh conditions of the prison and their fight to clear Shéhérazade's name.
The film has been lauded for its powerful story and strong performances by its two lead actresses. If you're looking for a touching and thought-provoking drama, Shéhérazade is worth a watch.
The Boy in the Striped Pyjamas
Director: Mark Herman
Writers: John Boyne, Mark Herman
Stars: Asa Butterfield, David Thewlis, Rupert Friend
Genres: Drama, War
The Boy in the Striped Pyjamas is a 2008 historical drama film set in World War II, based on the novel of the same name by John Boyne.
The film tells the story of Bruno, an eight-year-old German boy whose family moves from Berlin to Auschwitz concentration camp in Nazi-occupied Poland. He befriends a young Jewish boy named Shmuel, who is imprisoned in the camp.
Bruno's innocence and Shmuel's friendship help to humanize the horror of the Holocaust for the viewer. The film ends with a heart-wrenching twist that leaves the viewer contemplating the incomprehensible cruelty of the Nazi regime.
Director: Tom McCarthy
Writers: Tom McCarthy, Joe Tiboni
Stars: Paul Giamatti, Amy Ryan, Jeffrey Tambor
Genres: Comedy, Drama, Sport
In the movie Win Win, Paul Giamatti plays Mike Flaherty, a small-time lawyer, and wrestling coach, as a second job. He is a struggling family man who finds himself in a difficult situation. Desperate for money, Mike agrees to take on the case of Leo Poplar, an older man who requires a guardian. However, when Leo's grandson unexpectedly shows up, Mike is in a dilemma.
Mike soon realizes that the only way to keep Leo and his grandson happy is to rig the custody case in their favor. But as Mike's scheme starts to unravel, he must decide whether to do what is suitable for his family or what is right for Leo. It is a heartwarming story about family, friendship, and doing the right thing.
Directors: Richard Glatzer, Wash Westmoreland
Writers: Richard Glatzer, Wash Westmoreland, Lisa Genova
Stars: Julianne Moore, Alec Baldwin, Kristen Stewart
Alice Howland, a linguistics professor at Columbia University, starts to forget words. When she receives a diagnosis of Early-Onset Alzheimer's Disease, Alice and her family struggle to cope with the reality of her condition.
As the disease progresses, Alice becomes estranged from her family and friends. She eventually learns to accept her condition and finds comfort in her relationship with her husband, John. Together, they create new memories and cherish the ones they have from before Alice's diagnosis.
The film follows Alice over the course of several years as she deals with her Alzheimer's Disease. It is a moving and powerful story about love, family, and the strength of the human spirit.
Writer: Tom McCarthy
Stars: Richard Jenkins, Haaz Sleiman, Danai Gurira
The film stars Richard Jenkins, hailing from Connecticut, as Walter Vale, a college professor who discovers his career and life has become meaningless. To change this, he starts attending conventions and teaching classes again, only to find his world turned upside down when he meets Tarek (Haaz Sleiman), a young Syrian musician, and Tarek's girlfriend, Zainab (Danai Gurira). Over the course of a semester, the three forms an unlikely friendship that has life-changing consequences for them.
Director: Matthew Warchus
Writer: Stephen Beresford
Stars: Bill Nighy, Imelda Staunton, Dominic West
Genres: Biography, Comedy, Drama, History, Romance
Pride is a 2014 movie that tells the true story of how a group of gay and lesbian activists came to the aid of striking miners in Thatcher-era Britain. The group, which called themselves LGBT (lesbian, gay, bisexual, and transgender) people, raised money to support the miners and their families during the long strike.
The movie follows the group as they travel to the small village of mining named Onllwyn in Wales to deliver the money. There, they face opposition from the miners, who are wary of outsiders and suspicious of the motives of the LGBT activists. But as the two groups get to know each other, they begin to understand and respect each other, ultimately forming a strong alliance.
Pride is a heartwarming story of solidarity and acceptance. It highlights the importance of standing up for what you believe.
Director: Christine Jeffs
Writer: Megan Holley
Stars: Amy Adams, Emily Blunt, Alan Arkin
Genres: Comedy, Drama
In the 2008 film Sunshine Cleaning, Rose Lorkowski (Amy Adams) is a 30-something single mother who works as a full-time house cleaner. She lives in Albuquerque, New Mexico, with her son Oscar (Jason Spevack). Her sister Norah (Emily Blunt) is a rebellious teenager constantly getting into trouble.
Rose could not earn enough money from her current job, so she took advice from her former high-school boyfriend, Mac. Mac has strong connections as a police officer, so he recommends a crime scene cleanup job. She then decides to start her own business with Norah, which they call "Sunshine Cleaning." They begin by cleaning homes for people who have died and eventually expand their business to include crime scene cleanups.
The film follows Rose and Norah as they grow their business and deal with the ups and downs of their personal lives. Sunshine Cleaning is a heartwarming film about family, love, and overcoming adversity.
Director: Cary Joji Fukunaga
Writers: Moira Buffini, Charlotte Brontë
Stars: Mia Wasikowska, Michael Fassbender, Jamie Bell
Themes of love, morality, religion, and social class are explored in Jane Eyre, a 2011 movie based on the novel by Charlotte Brontë. The story follows the life of Jane, an orphan hired as a governess by the wealthy Mr. Rochester. Jane and Mr. Rochester eventually fall in love, but Mr. Rochester's dark secret complicates their relationship. The movie explores the challenges and obstacles that Jane and Mr. Rochester face in their relationship and ultimately ends with a happy resolution.
Director: Garth Davis
Writers: Saroo Brierley, Luke Davies
Stars: Dev Patel, Nicole Kidman, Rooney Mara
Genres: Biography, Drama
The movie Lion is based on the true story of Saroo Brierley, who was separated from his family in India at a young age and adopted by an Australian couple. Saroo spends years trying to find his way back home, armed only with his memories and a few clues. Along the way, he learns about love, loss, and the meaning of family.
The story of Lion is both heartwarming and heartbreaking, and it will stay with you long after the credits have rolled. If you are looking for a moving and inspiring film, check out Lion.
Director: Emilio Estevez
Writers: Emilio Estevez, Jack Hitt
Stars: Martin Sheen, Emilio Estevez, Deborah Kara Unger
The Way is a 2010 American drama film produced, written, and directed by Emilio Estevez and starring Martin Sheen. The film's storyline follows Thomas, a young American doctor. He travels to France to retrieve the ashes of his son, who died while crossing the historic Camino de Santiago pilgrimage route.
While making the journey, Tom meets other pilgrims from all walks of life who struggle with their issues. As they walk the ancient path together, they form deep bonds of friendship and begin to see the world in a new light. By the time they reach the end of the journey, they have all been transformed by the experience.
Director: Lenny Abrahamson
Writer: Emma Donoghue
Stars: Brie Larson, Jacob Tremblay, Sean Bridgers
Genres: Drama, Thriller
Room is a 2015 movie about a mother and her five-year-old son held captive in a small room. The mother, played by Brie Larson, does everything she can to protect her son and keep him safe. The movie is a gripping tale of survival and hope, and it will leave you wondering what you would do in a similar situation.
If you liked The Help, you could watch one of these 12 movies. Each film on this list has a similar feel, theme, or plot. They are all worth watching and will provide you with hours of enjoyment. So, sit back, relax, and enjoy these great movies.
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Latest consolidated Estonian Presidency Compromise Proposal published
Following many different versions of the Council of the European Union's "compromises" in relation to the Copyright Directive, a consolidated version is now available here. The compromises have been subject to much commentary. Of the compromise proposal dated 30 August 2017, commentators note the softening of the automated content filtering provisions, now being relegated to the recitals only (albeit still drafted in such a way as to ultimately leave the CJEU and domestic courts to determine their exact scope). Shortly thereafter on 26 September 2017 a compromise proposal was released which extended the text and data mining exception from "research organisations" only to also include "cultural heritage institutions". Commentators have argued that this minor tweak does not go far enough to align with the goals of the single market. A further compromise proposal dated 30 October 2017 was described as "chaos" by commentators, particularly in relation to Article 13 of the Copyright Directive. Here, the Council proposed to leave the existing safe harbour legislation of the E-Commerce Directive in place, ultimately resulting in two contradictory statutory regimes (with the revised proposal only applying to service providers with the "main purpose" of providing access to copyrighted works).


text:
\section{Introduction}
Graph structured-data plays an important role in describing the relationships between objects and has been widely utilized in modeling diverse real-world datasets including chemical compounds, molecular graph structures, protein-protein interaction networks, and social networks. Representation learning with graph structured-data usually requires one to learn effective representations that capture the graph structures as well as the features of nodes and edges. Recently, Graph Neural Network (GNN) attracted growing attention in graph representation learning in various domains such as text mining \cite{yao2019graph}, clinical decision making \cite{li2018classifying,mao2022medgcn}, and image processing \cite{mao2022imagegcn,garcia2017few}. GNN updates the node representation recursively by aggregating the neighborhood information of the node  \cite{hamilton2017inductive,morris2019weisfeiler,xu2019powerful,mao2020towards}. Finally, the representation of an entire graph is obtained through aggregating the node representations in the graph. The final node or graph representations can be fed into downstream learning tasks for end-to-end training.

Recently, researchers paid growing attention to improving the expressive power of GNNs, i.e., to obtain the representations that can effectively discriminate non-equivalent nodes or non-isomorphic graphs. In GNN, the aggregation rule plays a vital role in learning expressive representations for the nodes and the entire graph \cite{xu2019powerful,mao2020towards}. While various aggregation rules in GNN were proposed to achieve good performances in different tasks \cite{kipf2017semi,hamilton2017inductive,zhang2018end,xinyi2019capsule,wang2020haar}, \cite{xu2019powerful} indicated that GNNs can at most achieve the expressive power as the Weisfeiler-Lehman graph isomorphism test (WL test) and developed Graph Isomorphism Network (GIN) that can achieve the expressive power of the WL test by designing an injective neighborhood aggregation function and a graph-level readout function.

However, if a GNN possess a high expressive power, it would have a high risk of overfitting the training data when trained for a certain graph learning task, because it is likely to generate quite different distributions for graph or node representations between training data and test data. In this paper, we propose a GNN framework, Distribution Preserving GNN (DP-GNN), to alleviate the overfitting issue by preserving several kinds of distribution information in the node and graph representations. In the proposed framework, besides the major graph classification task, we also adopt 4 auxiliary tasks for distribution preserving. By the regularization of multiple distribution preserving tasks, DP-GNN is expected to achieve a better generalizability.

Figure \ref{fig:example} shows a toy example to illustrate the differences manifestations between unexpressive GNN, highly-expressive GNN, and DP-GNN in graph representation. In Figure \ref{fig:example}, the 4 graphs for classification are non-isomorphic to each other, A and C in training set, B and D in test set. The unexpressive GNN generates the same representations for different graphs, thus the classification task fails. While highly-expressive GNN can generate quite different representations for different graphs, a line that can separate A and C cannot correctly separate B and D, which implies the model trained on training data (A and C) cannot be generalized to test data (B and D). For the proposed DP-GNN, because the generated graph representation preserves the multiple kinds of distribution information which is beneficial to the graph classification task, it can not only generate different representations for different graphs, but also the graph representations on test data can be correctly separated by a line separating the training data, implying a better generalizability. Our contributions are summarized as follows:
\begin{itemize}
    \item We propose a distribution preserving GNN framework to improve the generalizability of expressive GNNs. To the best of our knowledge, this is the first study to explore the generalizability of expressive GNNs by distribution preserving.
    \item We adopt several auxiliary distribution preserving tasks to regularize the training process. By tuning the weight of different tasks, we can obtain the importance of different auxiliary tasks for graph classification task for different problems.
    \item The experimental results on several benchmark datasets for graph classification demonstrate that our model achieves state-of-the-art performances. Also, the experimental results validate the generalizability and expressive power of our model.
\end{itemize}

\begin{figure}
    \centering
    \includegraphics[width=0.8\linewidth,page=2]{MTLGNN.pdf}
    \caption{An example illustrating the differences between different GNNs in graph representations. With different colors indicating different node labels, the 4 graphs for classification are non-isomorphic to each other. Unexpressive GNN may map different graphs to the same representation. highly-expressive GNN can map different graphs to different representations, but the model trained on training data (A and C) cannot be generalized to test data (B and D). Our framework DP-GNN can map different graphs to different representations, and can have better generalizability. }
    \label{fig:example}
\end{figure}

\section{Related work}

\subsubsection{Expressive Power of GNN.}
Most modern GNNs were instances of the family of message passing neural networks \cite{gilmer2017neural} that follow a neighborhood aggregation strategy that recursively updates the representation of a node by aggregating representations of its neighbors and the node itself. The graph-level representation is obtained through aggregating the final representations of all the nodes in the graph. Recently, the expressive power of GNNs attracted increasing attentions. The expressive power of GNNs describes how a GNN model can distinguish different graphs or nodes. Most of the previous GNNs are designed based on empirical intuition, heuristics, or experimental trial-and-error until \cite{xu2019powerful} presented a theoretical framework for analyzing the expressive power of GNNs. \cite{xu2019powerful} showed that message passing GNNs can at most achieve the expressive power of the 1-WL test and developed GIN that can achieve this expressive power. By the conclusion of \cite{xu2019powerful}, to achieve expressive GNNs, it is critical to design an injective neighborhood aggregation function and an injective graph-level aggregation function. Inspired by the study of \cite{xu2019powerful}, \cite{mao2020towards} proposed to improve the expressiveness of GNN for attributed graphs by designing continuous injective set functions for neighborhood aggregation. \cite{maron2019provably} developed a GNN model that incorporates standard multi-layer perceptrons (MLPs) of the feature dimension and a matrix multiplication layer, possessing the expressiveness of 3-WL. A survey on the expressive power on GNN can be found in \cite{sato2020survey}.

Although an expressive GNN model has the ability to learn distinguishing graph representations, it has a high risk overfitting the training data in the end-to-end training for a certain graph learning task. While more and more studies focused on improving the expressiveness of GNNs, few studies in the literature were found to consider the generalizability of expressive GNNs. In this paper, we try to improve the generalizability of GNN by distribution preserving.

\subsubsection{Multi-task Learning.}
Multi-task learning (MTL) is a machine learning paradigm that jointly optimizes the model for multiple learning tasks. By leveraging the information contained in multiple related tasks, an MTL model is expected to learn each task more accurately.  A machine learning model optimized for a single task on training data may loss some relevant information captured in other related tasks, which could assist in generalizing the predictions to unseen data. Technically, MTL can help improve the generalization of models by introducing an inductive bias \cite{zhang2017survey}. A survey on MTL can be found in \cite{zhang2017survey}. Some works also applied MTL to graph representation learning. \cite{mao2022medgcn} employed a graph convolutional network to jointly learn two medical tasks, medication recommendation and lab test imputation. \cite{holtz4multi,xie2020multi} employed a GNN to jointly learn a node-level prediction task and a graph-level prediction task. \cite{wang2020multi} proposed a MTL framework for network embedding based on two tasks that respectively preserve the global and local structural information. \cite{xie2020multitask} presented a MTL framework that simultaneously performs multiple tasks including node classification and link prediction. However, the task of distribution preserving in graph representation has not been explored in-depth in the literature. This paper will consider the distribution preserving tasks for graph representation learning.

\section{Methods}
\subsection{Preliminaries}
\subsubsection{Problem Statement}
A graph $G$ is denoted as $(V,E)$, where $V$ is the node list (assume size $n$) corresponding to a node feature matrix $X\in {R}^{n\times d}$ and a list of node labels $L=[l_1,\cdots,l_n], l_i \in \{0,\cdots,C_N-1\}$ ($C_N$ is the number of classes of nodes), and $E$ is the set of edges corresponding an adjacency matrix $A\in\{0,1\}^{n\times n}$. For a set of labeled graphs $D = \{(G_1, y_1), \cdots, (G_N, y_N)\}$, where $y_i\in \{0,\cdots,C_G-1\}$ is the graph-level label associated with graph $G_i$, $C_G$ is the number of classes of graphs. For graph classification, we aim to learn a model $f(\cdot)$ that is able to correctly predict the graph label with the input of the graphs in $D$, i.e., $y=f(G),  \mbox{for } (G,y)\in D$. In this paper, $[x,y]$ denotes the concatenation of two vectors $x$ and $y$ or a vector with two elements $x$ and $y$. $\mathbf{x}[i]$ denotes the $i$th entry of vector $\mathbf{x}$.

\subsubsection{Expressive Graph Neural Networks}
An expressive GNN should be able to learn node or graph representations that incorporate information of the graph structure and node features, which are vital to discriminate different graphs. Most modern GNNs fall into the category of message passing GNNs. To get a node's representation, a message passing GNN first aggregates the node's neighbors' representations to achieve the neighborhood representation, and then combine the neighborhood representation and the node's current representation to achieve the new representation of the node. By 1 iteration, a node representation should contains information from its 1-hop neighborhood. After \textit{k} iterations of aggregation, a node representation captures the structural information of its \textit{k}-hop neighborhood. In this paper, unless otherwise stated, GNN defaults to a message passing GNN. Formally, the propagation rule of a GNN layer can be represented as
\begin{equation}\label{eq:aggregate}
H_{\mathcal{N}(v)}^{(k)} = f_{A}^{(k)}\left( \left\{H^{(k)}(w) | w\in \mathcal{N}(v) \right\}\right)
\end{equation}
\begin{equation}\label{eq:combine}
H^{(k+1)}(v) =
f_{C}^{(k)} \left(H^{(k)}(v) ,H_{\mathcal{N}(v)}^{(k)} \right)
\end{equation}
where $H^{(k)}(v)$ is the representation vector of node $v$ in the $k$th layer, and $H^{(0)}(v)$ is initialized with $X(v)$, the original feature vector of node $v$. $\mathcal{N}(v)$ is the neighborhood of $v$. $f_{A}^{(k)}(\cdot)$ is an aggregation function over a set of node representations; Eq. \ref{eq:aggregate} aggregates the neighbors' representations to achieve the neighborhood representation. Eq. \ref{eq:combine} combines the node's current representation and its neighborhood's representation in the $k$th layer to achieve the new node representation. Another aggregation function $f_{R}(\cdot)$ is employed to obtain the graph-level representation $H_G$ by aggregating the final representations of all nodes in the graph $G$, for a $K$-layer GNN, i.e.,
\begin{equation}\label{eq:readout}
H_G = f_{R} \left( \left\{H^{(K)}(v) | v\in G \right\}) \right)
\end{equation}

$H_G$ can be fed to a classifier for graph classification. To achieve a high expressive power for a GNN, $f_{A}(\cdot)$, $f_{C}(\cdot)$ and $f_{R}(\cdot)$ all should be injective \cite{xu2019powerful}. An injective function for $f_C(\cdot)$ that operates on two vectors can be easily achieved by concatenating the two vectors. Following the conclusion in \cite{mao2020towards}, an injective set function for $f_A(\cdot)$ or $f_R(\cdot)$ can be achieved by summing a certain transformation function over each element over the set, i.e.,
\begin{equation}\label{eq:poweragg}
f_{A}^{(k)}\left( \left\{H^{(k)}(w) | w\in \mathcal{N}(v) \right\}\right) = \sum_{w\in \mathcal{N}(v)}{\Phi^{(k)} \left(H^{(k)}(w) \right)}
\end{equation}
\begin{equation}\label{eq:readoutphi}
f_{R} \left( \left\{H^{(K)}(v) | v\in G \right\} \right) = \sum_{v\in G}{\Phi_G \left(H^{(K)}(v)\right)}
\end{equation}
where $\Phi^{(k)}(\cdot)$ and $\Phi_G$ are certain transformation functions that make the set function $f_A(\cdot)$ and $f_R(\cdot)$ continuous and injective, respectively.

\subsection{DP-GNN}
We adopt the expressive GNN architecture with learnable transformations in \cite{mao2020towards} as our backbone, where  $\Phi^{(k)}(\cdot)$ and $\Phi_G$ are approximated by learnable MLPs. Our propagation rule is
\begin{equation}\label{eq:MLPagg}
H^{(k+1)}(v)=
\mbox{MLP}_{C}^{(k)} \left( \left[H^{(k)}(v), \sum_{w\in \mathcal{N}(v)}{\mbox{MLP}_{T}^{(k)}\left(H^{(k)}(w)\right)}\right] \right)
\end{equation}
\begin{equation}\label{eq:readoutmlp}
H_G = \sum_{v\in G}{\mbox{MLP}_G \left(H^{(K)}(v)\right)}
\end{equation}
where $\mbox{MLP}_{T}^{(k)}$ and $\mbox{MLP}_{C}^{(k)}$ are the transformation function for neighborhood aggregation and the combine function for the \textit{k}th layer, respectively. $\mbox{MLP}_G$ is the transformation function for graph-level aggregation.

By the above propagation rule, an expressive GNN almost maps different graphs to different representations. Due to the different representations for different graphs, the representations of training graphs and test graphs are of high risk encountering distribution shift. A model optimized for a single task on the training set may overfit the training set. Since MTL can improve the generalization performance of multiple tasks when they are related \cite{zhang2017survey}, we use a MTL framework to achieve more generalized graph representations. Besides the major graph classification task, we set 4 auxiliary tasks for distribution preserving as a regularization method. The overview of the proposed DP-GNN model is illustrated in Figure \ref{fig:framework}, where an original graph gets its node representations through a multi-layer expressive GNN with propagation rule Eq. \ref{eq:MLPagg}, and the node representations are aggregated to get the graph representation by Eq. \ref{eq:readoutmlp}. We detail the tasks in Section \ref{sec:tasks}.

\begin{figure*}
    \centering
    \includegraphics[width=\linewidth,page=1]{MTLGNN.pdf}
    \caption{The overview of DP-GNN. An original graph gets its node representations through a multi-layer expressive GNN, and the node representations are aggregated to get the graph representation. Multiple tasks are used to constrain the node or graph representations. Task 1 is the major graph classification task; task 2 is to preserve the node label into node representation; task 3 is to preserve the graph label into node representations; task 4 is to preserve the information of node type distribution of the entire graph into the graph representation (i.e., [4,2] for 4 blue nodes and 2 yellow nodes in the graph); task 5 is to preserve the information of neighborhood distribution of a node into the node representation (i.e., [1,2] for one blue nodes and 2 yellow nodesin the neighborhood of A).}
    \label{fig:framework}
\end{figure*}

\subsection{Tasks for Distribution Preserving}\label{sec:tasks}
In this section, we introduce the major task and the 4 auxiliary tasks each of which corresponds to some distribution information in the graph.

\subsubsection{Major task: graph classification}
For a graph $G$, the final graph representation $H_G(G)$ is fed to an MLP for graph classification. For a total of $C_G$ classes of graphs, the MLP outputs a vector $S(G)$ of length $C_G$ corresponding to the support of the $C_G$ classes, i.e., $S(G) = \mbox{MLP}_1(H_G(G))$. In test process, the index of maximum $C_G$ is considered as the graph label. In training process, for the true label $y(G)$, we get the cross-entropy loss as
\begin{equation}
    \mathcal{L}_1(G) = -S(G)[y(G)]+\log \left(\sum_{i=0}^{C_G-1}\exp{(S(G)[i])} \right)
\end{equation}
\subsubsection{Task 2: node label preserving}
Usually, the graph label is related to the node label, thus a node representation that preserves the node label can be aggregated to an informative graph representation that helps the graph classification. Node label preserving is actually to preserve the probability distribution of a node's label in the node representation. For a node $v$, the final node representation $H(v)$ is fed to an MLP for node label prediction. For a total of $C_N$ classes of nodes, the MLP outputs a vector $S_N(v)$ of length $C_N$ corresponding to the support of the $C_N$ classes, i.e., $S_N(v) = \mbox{MLP}_2(H(v))$. In training process, for the true label $l(v)$, we get the cross-entropy loss as
\begin{equation}
    \mathcal{L}_2(v) = -S_N(v)[l(v)]+\log \left(\sum_{i=0}^{C_N-1}\exp{(S_N(v)[i])} \right)
\end{equation}

\subsubsection{Task 3: graph label preserving}
In a $k$-layer GNN, a node representation captures the information of its $k$-hop neighborhood, thus, for a graph with longest path not longer than $k$, each node representation can capture the information of the entire graph. Preserving the graph label in the node representation could be helpful to the graph classification. Graph label preserving is actually to preserve the probability distribution of a graph's label in the node representation. For a node $v$ in graph $G$, the final node representation $H(v)$ is fed to an MLP for graph label prediction. For a total of $C_G$ classes of graphs, the MLP outputs a vector $S_G(v)$ of length $C_G$ corresponding to the support of the $C_G$ classes, i.e., $S_G(v) = \mbox{MLP}_3(H(v))$. In training process, for the true label $y(G)$ of graph $G$, the cross-entropy loss is
\begin{equation}
    \mathcal{L}_3(v) = -S_G(v)[y(G)]+\log \left(\sum_{i=0}^{C_G-1}\exp{(S_G(v)[i])} \right)
\end{equation}

\subsubsection{Task 4: graph node distribution preserving}
Graph node distribution refers to the number of nodes of each type contained in the graph. The node distribution in a graph could affect the class of a graph. For example, the two classes in Figure \ref{fig:example} have different node distributions in a graph, Class 1 contains 2 red and 2 green nodes, while Class 2 contains 1 red and 3 green nodes. Preserving graph node distribution in the graph representation could help graph classification.
For a graph $G$, the final graph representation $H_G(G)$ is fed to an MLP for node distribution prediction. For a total of $C_N$ classes of nodes, the MLP outputs a vector $P_G(G)$ of length $C_N$ corresponding to the number of nodes in each of the $C_N$ classes, i.e., $P_G(G) = \mbox{MLP}_4(H_G(G))$. In training process, we can count the number of nodes of each type in the graph $G$ as the true node distribution $D_G(G)$, we get the mean squared error (MSE) loss as
\begin{equation}
    \mathcal{L}_4(G) = \frac{1}{C_N}\sum_{i=0}^{C_N-1} \left(P_G(G)[i]-D_G(G)[i] \right)^2
\end{equation}

\subsubsection{Task 5: neighborhood distribution preserving}
For a node $v$ in graph $G$, the neighborhood distribution refers to how many nodes of each type can be found in the neighbors of $v$ in graph $G$. Node representations that preserve the neighborhood distribution would help the aggregated graph representation aware of the neighborhood of each node. Thus, if the graph classification is related to the nodes' neighborhood, this task is helpful. For a node $v$ in graph $G$, the final node representation $H(v)$ is fed to an MLP for neighborhood distribution prediction. For a total of $C_N$ classes of nodes, the MLP outputs a vector $P_N(v)$ of length $C_N$ corresponding to the number of nodes in each of the $C_N$ classes, i.e., $P_N(v) = \mbox{MLP}_5(H(v))$. In training process, we can count the number of nodes of each type in the neighborhood of $v$ as the true neighborhood distribution $D_N(v)$, we get the MSE loss as
\begin{equation}
    \mathcal{L}_5(v) = \frac{1}{C_N}\sum_{i=0}^{C_N-1} \left(P_N(v)[i]-D_N(v)[i] \right)^2
\end{equation}

\subsection{Training Strategy}
We train the model by mini-batch, for a mini-batch of graphs $\mathcal{G}$, the graph-level losses ($\mathcal{L}_1$ and $\mathcal{L}_4$) are averaged over all the graphs in $\mathcal{G}$ and the node-level losses ($\mathcal{L}_2$, $\mathcal{L}_3$ and $\mathcal{L}_5$) are averaged over all the nodes in $\mathcal{G}$, i.e.,
\begin{equation}
    \mathcal{L}_1(G) = \frac{1}{|\mathcal{G}|}\sum_{G\in\mathcal{G}}\mathcal{L}_1(G)
\end{equation}
\begin{equation}
    \mathcal{L}_2(G) = \frac{1}{\sum_{G\in \mathcal{G}}|{G}|}\sum_{G\in\mathcal{G}}\sum_{v\in G}\mathcal{L}_2(v)
\end{equation}
\begin{equation}
    \mathcal{L}_3(G) = \frac{1}{\sum_{G\in \mathcal{G}}|{G}|}\sum_{G\in\mathcal{G}}\sum_{v\in G}\mathcal{L}_3(v)
\end{equation}
\begin{equation}
    \mathcal{L}_4(G) = \frac{1}{|\mathcal{G}|}\sum_{G\in\mathcal{G}}\mathcal{L}_4(G)
\end{equation}
\begin{equation}
    \mathcal{L}_5(G) = \frac{1}{\sum_{G\in \mathcal{G}}|{G}|}\sum_{G\in\mathcal{G}}\sum_{v\in G}\mathcal{L}_5(v)
\end{equation}
where $|\mathcal{G}|$ denotes the number of graphs in batch $\mathcal{G}$, $|G|$ denotes the number of nodes in graph $G$.

we combine all the losses on graph-level and node-level to get a final loss function. Also, we introduce 4 hyper-parameters indicating the weight of each auxiliary task contributing to the major graph classification task. The final loss for a mini-batch of graphs $\mathcal{G}$ is defined as
\begin{equation}
    \mathcal{L}(\mathcal{G}) = \mathcal{L}_1(\mathcal{G}) + \lambda_2 \mathcal{L}_2(\mathcal{G}) + \lambda_3\mathcal{L}_3(\mathcal{G}) + \lambda_4\mathcal{L}_4(\mathcal{G}) + \lambda_5\mathcal{L}_5(\mathcal{G})
\end{equation}
where $\lambda_i$ denotes the weight of task $i$ for the major graph classification task.

\textbf{Discussion.}
Although our auxiliary tasks share some similar properties with the tasks in GNN node-level pre-training stage in \cite{hu2019strategies}, our multi-task graph learning framework is different from the self-supervised pretraining GNN framework in at least 3 aspects. (1) We have different workflows. Pretraining GNN follows the two-stage pretraining and fine-tuning framework, while our model does not have a pretraining process, containing only one training stage. (2) Pretraining GNN requires a large-scale graph dataset for pretraining, while our model does not. (3) For pretraining GNN, the node-level self-supervised pretraining and graph-level multi-task supervised pretraining are performed sequentially, and the fine-tuning process is performed only for the target task without any auxiliary tasks; while in our framework, the auxiliary tasks and the target task are trained jointly with a combined loss function.

\section{Experiments}
In this section, we conduct experiments to evaluate the expressive power and generalizability of our model. The expressive power and generalizability correspond to the performance on training set and test set, respectively.

\subsection{Experimental setup} \label{sec:expset}
\subsubsection{Datasets.} Four widely used benchmark datasets are involved in our experiments, including MUTAG, PTC, NCI1 and PROTEINS. A summary of these datasets is listed in Table \ref{tab:dataset}. We use one-hot encodings of node labels as the initial input features.

\begin{table*}[!t]
  \centering
    \begin{tabular}{l|c|c|c|c|c|c|l}
    \toprule
          & \#G & \#$C_G$ & \#$C_N$ & AvgN & AvgE  & MaxNeighb  & Source \\
\midrule
  MUTAG &188 &	2 &	7 &17.93&	19.79 & 4  & \cite{debnath1991structure,kriege2012subgraph}\\
  PTC &344	&2 & 19	&14.29&	14.69 & 4 &  \cite{helma2001predictive,kriege2012subgraph}\\
  NCI1 &4110&	2	& 37  &29.87&	32.30 & 4  & \cite{wale2008comparison,shervashidze2011weisfeiler}\\
  PROTEINS &1113&	2 &  3	&39.06&	72.82 & 25 & \cite{borgwardt2005protein,dobson2003distinguishing}\\
    \bottomrule
    \end{tabular}%
  \caption{Dataset information. all the Dataset can be available at \url{https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets}. \#G=number of graphs. \#$C_G$=number of graph classes. \#$C_N$=number of node classes. AvgN=average number of nodes in one graph. AvgE=average number of edges in one graph. MaxNeighb is the max 1-hop neighbors in all the nodes. }
  \label{tab:dataset}%
\end{table*}%

\subsubsection{Implementation details.}
Since the initial node features are one-hot encodings, the summation with identical transformation is injective, thus we set whether the transformation function in the first layer is identical or an MLP as an optional hyperparameter. We set 5 GNN layers in our model, all MLPs contained in GNN layers or for prediction in the model had 1 hidden layer. In every hidden layer, we applied a batch normalization followed by a ReLU activation function. The Adam optimizer with learning rate decayed by 0.5 every 50 epochs. For each dataset, we trained the model for 300 epochs and recorded the performances on training set and validation set for each epoch. The batch size is 32. The drop out ratio is 0. The search space of hyper-parameters we tuned for each dataset are: (1) the number of hidden units \{16, 32, 64\}; (2) initial learning rate \{0.01, 0.001\}; {3} the transformation function in the first layer \{identical, MLP\}; (4) the weight of the 4 auxiliary tasks $\lambda_2,\lambda_3,\lambda_4,\lambda_5 \in \{0,1\}$. For each dataset, we followed the standard 10-fold cross validation protocol and splits from \cite{xu2019powerful}. Following the previous work \cite{xu2019powerful,maron2019provably,mao2020towards}, we reported the best averaged validation accuracy across the 10 folds for a fair comparison. Our experiments were run in 10 Tesla V100 GPUs.

\subsubsection{Compared Methods.}
We compared our model with a number of state-of-the-art methods listed in the first column in Table \ref{tab:result} for graph classification. Note that ExpGNN \cite{mao2020towards} and MTRL \cite{xie2020multi} fall in our framework. If the weights of all auxiliary tasks are 0, our model reduces to ExpGNN; if we only consider the node label preserving task, our model reduces to MTRL with ExpGNN as backbone. Besides, the compared methods also include the latest work on deep learning on graphs (e.g., \cite{maron2019provably,wang2020haar,xu2019powerful,morris2019weisfeiler}) and classical graph kernel methods (e.g., \cite{shervashidze2011weisfeiler,shervashidze2009efficient,yanardag2015deep}). Please refer to the corresponding papers for detailed introductions of the methods. For ExpGNN and MTRL we ran the experiments to get the results in our settings. For other baselines, we report the accuracy results in the original papers.

\subsection{Results}
We evaluate our model on both training set and test set. The performances on test set reflect the generalizability of a model, and the performance on training set usually reflects the expressive power of a model.

\subsubsection{Test performance.}
Table \ref{tab:result} lists the accuracies of our model compared with the baselines. The hyper-parameters that achieve the listed performances are attached in Table \ref{tab:hyperparameter} for reproduction. From Table \ref{tab:result}, while no one method can achieve the best for all the 4 datasets, no other models except our model DP-GNN can achieve the top 3 among the 21 methods for all the 4 datasets, and DP-GNN performs the best on PTC dataset. DP-GNN can consistently perform better than message passing GNN models that represent high expressive power, such as ExpGNN, MTRL, GIN, validating the efficacy of the distribution preserving task in our framework.

\begin{table}[!t]

\scriptsize
  \centering
  \caption{Accuracy (\%) for graph classification with 10-fold cross validation (mean$\pm$std). The best performances are underlined. Top 3 performances on each dataset are bolded.}
    \begin{tabular}{l|c|c|c|c}
    \toprule
      Models    & MUTAG & PTC   & NCI1  & PROTEINS \\
    \midrule
    DP-GNN (ours) & \textbf{91.1$\pm$6.2} & \underline{\textbf{67.3$\pm$6.9}} & \textbf{84.2$\pm$1.9} & \textbf{76.9$\pm$5.3} \\
    ExpGNN \cite{mao2020towards} & 90.0$\pm$6.9 & 65.6$\pm$4.8 & 83.5$\pm$2.2 & 76.5$\pm$4.5 \\
    MTRL  \cite{xie2020multi}  & 90.0$\pm$6.0 & 65.0$\pm$7.4 & \textbf{83.7$\pm$2.3} & 76.5$\pm$5.0 \\
    GIN  \cite{xu2019powerful}  & 89.4$\pm$5.6 & 64.6$\pm$7.0 & 82.7$\pm$1.7 & 76.2$\pm$2.8 \\
    GCN  \cite{kipf2017semi}  & 85.6$\pm$5.8 & 64.2$\pm$4.3 & 80.2$\pm$2.0 & 76.0$\pm$3.2 \\
    GraphSAGE  \cite{hamilton2017inductive} & 85.1$\pm$7.6 & 63.9$\pm$7.7 & 77.7$\pm$1.5 & 75.9$\pm$3.2 \\
    3WL GNN  \cite{maron2019provably} & 90.5$\pm$8.7 & \textbf{66.2$\pm$6.5} & 83.2$\pm$1.1 & \textbf{77.2$\pm$4.7} \\
    HaarPool  \cite{wang2020haar} & 90.0$\pm$3.6 &   -    & 78.6$\pm$0.5 & \underline{\textbf{80.4$\pm$1.8}}\\
    PSCN  \cite{niepert2016learning} & \underline{\textbf{92.6$\pm$4.2}} & 60.0$\pm$4.8 & 78.6$\pm$1.9 & 75.9$\pm$2.8 \\
    DCNN  \cite{atwood2016diffusion}  & 67.0  & 56.6  & 62.6  & 61.3 \\
    DGCNN  \cite{zhang2018end} & 85.8$\pm$1.7 & 58.6$\pm$2.5 & 74.4$\pm$0.5 & 75.5$\pm$0.9 \\
    CapsGNN  \cite{xinyi2019capsule} & 86.7$\pm$6.9 & -     & 78.3$\pm$1.5 & 76.3$\pm$3.6 \\
    GCAPS-CNN  \cite{verma2018graph} & -     & \textbf{66.0$\pm$5.9} & 82.7$\pm$2.4 & 76.4$\pm$4.2 \\
    IEGN  \cite{Maron2019InvariantAE}  & 84.6$\pm$1 & 59.5$\pm$7.3 & 73.7$\pm$2.6 & 75.2$\pm$4.3 \\
    ECC  \cite{simonovsky2017dynamic}  & 76.11 &   -    & 76.82 & - \\
    1-2-3 GNN  \cite{morris2019weisfeiler} & 86.1  & 60.9  & 76.2  & 75.5 \\
    \midrule
    FGSD (Verma \cite{verma2017hunt}  & \textbf{92.12} & 62.8  & 79.8  & 73.42 \\
    WL  \cite{shervashidze2011weisfeiler}   & 83.8$\pm$1.5 &   -    & \underline{\textbf{84.5$\pm$0.4}} & - \\
    GK  \cite{shervashidze2009efficient}   & 81.6$\pm$2.1 & 57.3$\pm$1.4 & 62.5$\pm$0.3 & 71.7$\pm$0.5 \\
    Graph2vec  \cite{narayanan2017graph2vec} & 83.1$\pm$9.2 & 60.2$\pm$6.9 & 73.2$\pm$1.8 & 73.3$\pm$2.0 \\
    DGK \cite{yanardag2015deep}  & 87.4$\pm$2.7 & 60.1$\pm$2.5 & 80.3$\pm$0.5 & 75.7$\pm$0.5 \\
    \bottomrule
    \end{tabular}%

  \label{tab:result}%
\end{table}%

\begin{table}[htbp]
\small
  \centering
  \caption{The hyper-parameters corresponding to the best performance}
    \begin{tabular}{l|c|c|c|c}
    \toprule
          & MUTAG & PTC   & NCI1  & PROTEINS \\
          \midrule
    hidden dim & 32    & 64    & 32    & 16 \\
    learning rate & 0.01  & 0.01  & 0.001 & 0.01 \\
    first TF & identical & identical & MLP   & identical \\
    \midrule
    $\lambda_2$ & 1     & 0     & 1     & 1 \\
    $\lambda_3$ & 0     & 0     & 1     & 1 \\
    $\lambda_4$ & 0     & 1     & 1     & 0 \\
    $\lambda_5$ & 1     & 1     & 0     & 1 \\
    \midrule
    accuracy & 0.9111 & 0.6735 & 0.8421 & 0.7694 \\
    \bottomrule
    \end{tabular}%
  \label{tab:hyperparameter}%
\end{table}%

\subsubsection{Training curve.}
We also averaged the accuracies on both training set and validation set across the 10 folds for each epoch to show how the performances changed in the training process. Figure \ref{fig:trainingcurve} illustrates how the accuracy vary along with the training epoch on both training set and test set. From Figure \ref{fig:trainingcurve}, on both MUTAG dataset (\ref{fig:mutagcurve}) and PTC dataset (\ref{fig:ptccurve}), the training accuracies of DP-GNN and ExpGNN are approaching 1 in the final epoch, validating the high expressive power of DP-GNN and ExpGNN. Also, Figure \ref{fig:trainingcurve} demonstrates the rank of models in expressive power is ExpGNN$\approx$DP-GNN$>$GIN$>$GCN, which is consistent with the theoretical analysis about expressive power \cite{mao2020towards,xu2019powerful}. DP-GNN is of high expressive power because DP-GNN use the backbone of ExpGNN. For the test accuracy, DP-GNN consistently performs best after a certain epoch when the model are trained adequately for both MUTAG and PTC datasets, validating the generalizability of DP-GNN.

\begin{figure}[!t]
  \centering
  \subfloat[MUTAG]{\includegraphics[width=.45\linewidth,page=1]{MUTAG_training_curve.pdf}\label{fig:mutagcurve}}
  \subfloat[PTC]{\includegraphics[width=.45\linewidth,page=1]{PTC_training_curve.pdf}\label{fig:ptccurve}}
  \caption{The accuracy varying with the training epoch on training set and test set in the training process.}
  \label{fig:trainingcurve}
\end{figure}

\subsubsection{TSNE visualization.}
We also showed the t-SNE visualization of the generated embeddings for DP-GNN and ExpGNN on MUTAG dataset in Figure \ref{fig:tsnevisual}. While both representations are visually separable for graph classification, we find that the representations generated by ExpGNN \ref{fig:tsneexpgnn} are more compact than by DP-GNN \ref{fig:tsnedpgnn}. Because the representations generated by ExpGNN were trained by optimizing the major graph classification tasks, and the representations of DP-GNN were trained by optimizing multiple auxiliary tasks as well as the major graph classification tasks, the representations of DP-GNN should contain more information that could used for the auxiliary tasks, thus not as compact as ExpGNN. However, compact representations on training set cannot ensure the compactness in test set, and usually cause more distribution shift. Non-compact and easily separable representations could have better generalizability.

To verify the distribution shift between training set and test set, we also plot the TSNE visualization of DP-GNN and ExpGNN on training set and test set for PTC dataset in Figure \ref{fig:tsnedpgnn_trte} and \ref{fig:tsneexpgnn_trte}, where DP-GNN indeed shows less distribution shift between training set and test set than ExpGNN.

\begin{figure}[!t]
  \centering
  \subfloat[DP-GNN]{\includegraphics[width=.25\linewidth,page=1]{MUTAG_dp_TSNE.pdf}\label{fig:tsnedpgnn}}
  \subfloat[ExpGNN]{\includegraphics[width=.25\linewidth,page=1]{MUTAG_exp_TSNE.pdf}\label{fig:tsneexpgnn}}
  \subfloat[DP-GNN]{\includegraphics[width=.25\linewidth,page=1]{PTC_dp_trte.pdf}\label{fig:tsnedpgnn_trte}}
  \subfloat[ExpGNN]{\includegraphics[width=.25\linewidth,page=1]{PTC_exp_trte.pdf}\label{fig:tsneexpgnn_trte}}
  \caption{t-SNE visualizations. (a-b) t-SNE visualization of different classes in MUTAG dataset; (c-d) t-SNE visualization of training set and test set in PTC dataset.}
  \label{fig:tsnevisual}
\end{figure}

\subsection{Ablation Study}
We recognize that for different graph classification tasks, not all the auxiliary tasks are helpful. For example, if the graphs are classified by the node numbers or if all the graphs contain only one kind of nodes, then task 2, i.e., node label preserving can do nothing with the major task. To determine what tasks are helpful to the major graph classification tasks on different datasets, we have done an ablation study. In this study, for each of the 4 auxiliary tasks, we run the model to determine if the single auxiliary task contributes to the major task independently, and then we combine all the helpful tasks for another run to see if the combination of helpful tasks contributes more to the performance of the major task. The results are listed in Table \ref{tab:ablation}, where we can see that the combination of independently helpful tasks do contribute more to the performance of the major task. Specifically, Task 2 and 5 are helpful on MUTAG and PROTEINS datasets; Task 4 and 5 are helpful on PTC dataset; Task 2, 3 and 4 are helpful on NCI1 dataset. We also list the best accuracy and the corresponding $\lambda$ in the last row of Table \ref{tab:ablation} for comparison.

\begin{table}[!t]
\small
  \centering
  \caption{The weights of auxiliary tasks relevant to the major task.$\lambda=[\lambda_2,\lambda_3,\lambda_4,\lambda_5]$}
    \begin{tabular}{c|c|c|c|c}
    \toprule
    $\lambda$ & MUTAG & PTC   & NCI1  & PROTEINS \\
    \midrule
    $[0,0,0,0]$ & 0.9000 & 0.6558 & 0.8348 & 0.7649 \\
    \midrule
    $[1,0,0,0]$ & \textbf{0.9000} & 0.6500 & \textbf{0.8375} & \textbf{0.7649} \\
    $[0,1,0,0]$ & 0.8889 & 0.6500 & \textbf{0.8350} & 0.7595 \\
    $[0,0,1,0]$ & 0.8944 & \textbf{0.6618} & \textbf{0.8367} & 0.7586 \\
    $[0,0,0,1]$ & \textbf{0.9056} & \textbf{0.6735} & 0.8343 & \textbf{0.7667} \\
    \midrule
    combined $\lambda$ & $[1,0,0,1]$ & $[0,0,1,1]$ & $[1,1,1,0]$ & $[1,0,0,1]$ \\
    accuracy & \textbf{0.9111} & \textbf{0.6735} & \textbf{0.8421} & \textbf{0.7685} \\
    \midrule
    best $\lambda$ & $[1,0,0,1]$ & $[0,0,1,1]$ & $[1,1,1,0]$ & $[1,1,0,1]$ \\
    best accuracy & \textbf{0.9111} & \textbf{0.6735} & \textbf{0.8421} & \textbf{0.7694} \\

    \bottomrule
    \end{tabular}%

  \label{tab:ablation}%
\end{table}%

\section{Conclusion}
In this paper, we propose DP-GNN to improve the generalizability of expressive GNN models by preserving several kinds of distribution information in the learned graph and node representations. The model is implemented by a multi-task learning framework where 4 auxiliary tasks are employed for distribution preservation. Since DP-GNN is built based on a highly-expressive GNN model, it can also achieve high expressive power. We validate the proposed DP-GNN for graph classification on multiple benchmark datasets. The accuracy performances of DP-GNN on training set are approaching 1, confirming the high expressive power of DP-GNN. The experimental results on test set demonstrate that our model achieves state-of-the-art performances on most of the benchmarks, validating the generalizability.

\bibliographystyle{named}

redpajama_set_name: RedPajamaArXiv


Raid the recycling bin and show off what an upcycling-savvy mother you are while providing your little pirate with endless hours of entertainment. Upcycling is the process of converting useless items into new materials, and with your upcycling skills, your youngster can sail the mighty seas with a cardboard ship created by you. You can enlist your preschooler's help in the ship-building project so he can help build and decorate it, or you can surprise him with the cardboard vessel when he awakes from his afternoon nap.
Lay a large cardboard box down on one of its largest sides. You want it to be as roomy as possible to give your youngster plenty of play space. Cut off the top of the box, because this ship doesn't need a roof.
Draw three circles on the starboard side of the ship — the right side — and then cut them out to make portholes. Draw a large rectangle on the port side — the left side — and cut it out so your kiddo can get onto his ship. At this point, you already have the basics of the ship complete.
Cut another piece of cardboard from a large sheet. Cut out a triangle shape for a flag and duct tape the flag to an empty wrapping paper roll. Have your kiddo help decorate the flag with markers, crayons or embellishments.
Attach the flag to the ship with duct tape. You can give the ship a paint job to cover the duct tape or you can or enlist your toddler's help to decorate it.
Cut one last piece of cardboard into a rectangle about 12 by 4 inches in size. Give your kiddo's ship a name and write it across the cardboard sign. Glue the sign to the bow of the ship and that's it — a brand-new ship made out of not brand-new materials that will keep your youngster sailing the mighty seas for weeks to come!
Playhouses You Can Build: Indoor and Backyard Designs; David Stiles, et al.
What Can Toddlers Make Out of a Cardboard Box Besides a Fort?


It happens quickly. The temperature starts to drop. Leaves fall from the trees. You notice the skies turning grayer. It is disconcerting that it still feel like it's summer, but deep down inside you know everything is changing.
The memories of youth fill you with the both the dread and exhilaration of the back-to-school time period. Even as adults, we experience the twinge of regret over the loss of the summer, a little sadness, trepidation and, yet, hope of a new start. The kids go back to class and we adults summon up the strength and courage to return to business-as-usual in the office.
Selfishly, I look forward to September. As an executive recruiter, this time represents a return to the familiar steady rhythm of recruiting. During the summertime, the interview process is plagued by starts and stops due to everyone's vacation schedules. Just when a candidate gathers momentum in the search process, a key interviewer goes on vacation. When they return, another person is out of the office and can be found lounging on a beach. Then, the candidate takes their family on a trip to Disney.
The start of September substantially changes this pattern. It is an unwritten rule that it is acceptable to take your time off during July and especially late August. It is seen as rude and uncouth to coast through the summertime when things are generally slow and then go on vacation in September when the real work starts up again.
You would like to believe that hiring patterns are more sophisticated than being driven by the seasons and vacation schedules, but the they are not. Although I have seen a steady flow of interesting new jobs come in, met great candidates and made a number of placements, the pace was slow, winding, bumpy and—at times—brutally slow moving.
With the summer winding down, reality kicks in and you quickly recall that you actually hate your job. Over the summertime, it was easy to see past the mundane work, lack of opportunities to advance, rumors of layoffs, irritating co-workers, indifferent management and a mediocre company that is floundering. Your mind was focused on the nice weather and all the fun things you had planned. Now, you have no other choice than to confront this cold, cruel reality and will probably start to think about moving onto another job.
Since human resource professionals, executive management and hiring managers have been on vacations and generally working at a slower pace and enjoying half-day Fridays, there has been a pent-up demand brewing. With work now picking up, managers recognize and feel the pressure of having an opening and will expedite the hiring process to bring aboard new employees to alleviate the burdens of tasks and projects that were set aside over the last couple of months. Management will notice this pattern (and if they don't, they will certainly hear complaints from their overworked employees) and realize that they should move quickly to tap into the market of new arrivals into the job market.
The stage is set to hire. Your team is back in the office and, presumably, well rested and ready to work. Candidates are returning full force into the job market. Both sides have a vested interest in making this happen.
Be advised, there is a only small window of time to hire. We have September, October and the first half of December. Once the winter holiday season starts, it is similar to the summertime (except for the cold and snow) in that everything slows down again.
There are other challenges to face in the autumn season. As we inch closer to the end of the year, bonuses and raises become an issue. In certain market sectors, such as Wall Street, bonuses play a big part in a person's compensation. If someone anticipates a large bonus, they will want the hiring company to make good on what they will leave behind. Under best circumstances, if you interview in September, get an offer in October, then give two-plus weeks notice, you will only be working at the new company for about two months. The company is not keen on offering a full bonus for this short amount of time. Additionally, the job seeker will maintain that he/she will be expecting a raise and would want the company to factor that into the salary offer. The company may also question if they are really in line for a raise and, if so, is it as lush as the candidate purports it to be?
Although it is widely reported that we have full employment in the United States, companies don't act like this is a true fact. If there really is full employment, it would suggest that corporations would fight to get the best and brightest people. This should entail offering higher salaries to entice people to join their firm and not lose out to their competitors. The companies would also buy-out or guarantee bonuses to people who are switching jobs, but they are not doing this. Also, you would believe that the firms would raise the salaries for existing employees to keep them happy and remaining at the company. This is not happening either.
It is a conundrum that while there appears to be plenty of job opportunities available, companies are generally not being flexible or generous with their compensation offers. In particular, companies are reticent to buyout bonuses and offer substantial increases in salaries to job candidates.
A new law passed in many states, and adopted in others, stating that companies cannot ask what a job seeker is earning or pry into their past salary history. The law was predicated upon the belief that people are placed in a financial disadvantage and low-balled when the company knows their compensation. The theory behind the law, in part, is that a person should be paid what they are worth notwithstanding what their current salary is at the time of the interview. The law has good intentions, but also has unintended consequences. The salary negotiations become similar to playing poker. Each party is not fully aware of the other person's cards. There is an element of bluffing, lying, exaggeration and disbelief. Candidates feel that the company is not coming clean about the actual salary that could be paid and company representatives believe that job seekers are "swinging for the fences" trying to get a huge increase in pay. This leads, at times, to awkward negotiations, distrust and deals falling apart with bad feelings on both sides.
It is becoming pretty obvious that companies are trying to save money by moving people to lower-cost locations—both within the United States and to other countries. If the jobs are not relocated, technology is quickly deployed to displace costly human beings who demand silly and frivolous things, such as lunch breaks, holidays off from work and occasional chit-chat and gossip with colleagues. If these tactics don't save enough money, companies have embarked upon a campaign to push out experienced (code word for 'older' or 'more seasoned' employees).
So, goodbye summertime and welcome to the fall!


How to publish or unpublish dam assets for a period of time in CQ ?
As for the pages, using the on/off time properties in the way to go .
As for the current release, you will have to use an Asset Editor page to edit them.
The process is documented in this documentation page (1). Search for the "Title" component section.
Summary : To edit the on/off time properties, edit the title component, and uncheck the "Hide edit button". Then, users can edit the Title by clicking the Pencil icon, and enter On and Off Times to turn the asset on and off.
Starting the next release of CQ : 5.6, the edition of those properties will be performed from the damadmin UI.
The mechanism behind the definition of an "on" or and "off" period for assets, is to trigger corresponding Activation or Deactivation request towards the publishers, in order to physically add, or remove the Asset on the instances.
This way, publishers won't be able to render the deactivated asset via any component or Asset Search feature during an "off" period.
To achieve this, the "On-/Offtime reached" trigger has to enabled on your standard replication agents, see this doc page (2) .
An asset must not be accessible during Monday the next week (let's say 20XX/07/22), between 8am and 7pm . The Asset is currently published.
- Set the off time to 20XX/07/22 at 08:00 and the ontime to 20XX/07/22 at 19:00 .
Thanks to the trigger, a Deactivation request will be issued at 8am on this day, removing the asset from publishers.
An Activation request will be sent at 7pm the same day, to make the Asset available again.
Note : the same trigger must be enabled on your dispatcher flush agents to invalidate the cache when the on/off time period boundaries are reached.
Please raise a Daycare ticket if you have problems to use the feature or need additionnal informations.


text:
Bay Area Solidarity Summer (BASS)
Who Is BASS?
Bay Area Solidarity Summer is organized by a volunteer group of progressive activists committed to supporting and mentoring the next generation of young South Asian organizers. Contact us if you're interested in volunteering or have other questions. Our 2020 core organizers include:
Shams-Tabraiz Muzaffar
Shams-Tabraiz Muzaffar is a Youth Solidarity Summer alum and has worked with development, humanitarian aid, research, education, and labor organizations in a range of countries including Pakistan, Eritrea, Ghana, South Africa, and the United States. He is also co-director of the forthcoming documentary film Son of a Bug. He loves ice cream and milkshakes.
Anu Mandavilli
Anu Mandavilli has been involved in social justice work with community organizations, social movements, and labor unions in India and the U.S. She was part of the Mining Zone Peoples' Solidarity Group research collective, working with communities in Orissa, India, fighting land grabs by steel giant POSCO. She has worked on campaigns around Hindu nationalism and corporate accountability with Friends of South Asia and the Free Binayak Sen campaign. Locally, she works with the San Jose Peace and Justice Center and the Coalition for Justice and Accountability towards civilian oversight of the SJPD.
Palvinder Kaur
Palvinder Kaur is a public health, education, and social justice enthusiast. She was previously the Director of Development and Programs for Jakara Movement. She is passionate about building community power and inspiring changemakers. She enjoys photography and accepts tips to improve her skills!
Anirvan Chatterjee
Anirvan Chatterjee is a techie, activist, and storyteller. He curates the Berkeley South Asian Radical History Walking Tour, and works with ASATA and the Desi LGBTQ Helpline. He used to run a tech startup, but now he spends his weekends trawling through old newspapers, trying to uncover secret Desi histories.
Asha Mehta
Asha Mehta has done leadership development with nonprofits and movements from across the country. She has previously been a community school coordinator, high school teacher, youth empowerment program director, and executive director. She loves to spend time listening to music with her son, reading, and swimming. She secretly dreams of being a standup comic one day.


Lawmakers are considering efforts to increase scrutiny over how municipalities utilize tax abatements to attract developers, a practice known as payments in lieu of taxes.
Towns have been using PILOT agreements for years in order to draw businesses, jobs and investments into otherwise overlooked neighborhoods. Proponents argue that it's been an invaluable tool for revitalizing towns such as Atlantic City, Jersey City, Newark and New Brunswick.
But the PILOT program, by design, leaves no money for local school districts: 95 percent of the payments go to the town, and the other 5 percent to the county.
On Monday, lawmakers pushed through a measure, Senate Bill 1701, which would require the town and developers each to produce their own cost-benefit analysis on how the PILOT agreement would impact the local tax revenue, especially school funding.
The measure passed unanimously in the Senate Budget and Appropriations Committee, to the opposition of the New Jersey League of Municipalities, who argued that the bill would add on more expenses for developers and the towns.
"Any time you propose changes that will drive up application costs, drive up administrative costs, we're concerned because it affects the overall viability of the PILOT," said NJLM Associate Executive Director Michael Cerra.
Sweeney's proposal comes at a time when he's looking to revamp the state's school funding by "equalizing school aid," that is, taking money from "overfunded districts" and spreading it out to districts more in need of those funds.
The Senate President has said he'd be willing to shut down the state government for the second year in a row if he doesn't have his way on school funding.
With the potential for PILOT laws to be reworked to allow tax money to be redirected towards local schools, that could take the pressure off the state, at least according to Assemblyman Edward Thomson, R-30th District.
In May, Thomson unveiled Assembly Bill 3969, which would require tax-exempt developers to rework the funding distribution to include money earmarked for school taxes. Another bill Thomson sponsored would require the value of tax exempt properties to be included in the state's calculations for how much school funding should go towards a particular town.
Both bills were referred to the Assembly State and Local Government Committee in May, but neither have received a vote.


text:
exports.config = {
  seleniumAddress: 'http://localhost:4444/wd/hub',
  specs: ['test/**.spec.js'],
  capabilities: {
    browserName: 'firefox'
  }
};

redpajama_set_name: RedPajamaGithub


text:
Q: Why RxJava2 does not emitter data to the thread I have read the RxJava's doc. and try the demo below:
The Output is d = [0]d = false
May I know the reason that: why the data does not emitter to the Observer
RxJava version: 2.2.6
        Observable.just("aa","bbb")
                .observeOn(Schedulers.newThread())
                .map(s -> {
                    System.out.println("s = [" + s + "]");
                    return s.toUpperCase();
                })
                .subscribeOn(Schedulers.single())
                .observeOn(Schedulers.io())
                .subscribe(new Observer<String>() {
                    @Override
                    public void onSubscribe(Disposable d) {
                        System.out.println("d = [" + d + "]"+"d = "+ d.isDisposed());
                    }

                    @Override
                    public void onNext(String s) {
                        System.out.println("s = [" + s + "]");
                    }

                    @Override
                    public void onError(Throwable e) {

                    }

                    @Override
                    public void onComplete() {

                    }
                });

A: With all the thread switches you have added to your subscription your subscription is not running on the main thread anymore. This also means that nothing is stopping the main thread anymore and you application quits before it could print the data you want.
For testing purposes you can add a Thread.sleep(1000); at the end of your main() method (where I suppose you have added this code). Then you will see the output from the System.out.println() statements. Other solutions are to not use subscribeOn() or observeOn() and stay on the current thread. Also, you can use blockingSubscribe() to wait for the subscription to finish before continuing on the main thread.

redpajama_set_name: RedPajamaStackExchange


The annual Robben Island Triathlon adds a 14 km run this year to the swim-bike-run formula of the competition.
The triathlon includes a harbour swim and then bicycling and running past many of the island's key tourists sites, including the hard labour lime pits, the lighthouse and the world war two cannon. The competition comes in two versions; the easier 4 m swim: 14 km cycle: 4 km run and the tougher 8:28:8.
This year corporate packages have been introduced for the first time and participants can also book over-night accommodation and a meal in the prison itself.
Robben Island is now a World Heritage Site but it has a history as a leper colony, quarantine centre and most recently as a maximum security political prison from 1961 until 1991 under the apartheid regime. Its most famous prisoner was Nelson Mandela who was interned there for 18 years.
It is also a site of Muslim pilgrimage to the Moturu Kramat, a shrine built in 1969 to commemorate Sayed Abdurahman Moturu, one of Cape Town's first imans who was exiled to the island and died there in 1754.


Amsterdam, Netherlands - April 2019; The Vondelpark on a bright spring afternoon.
Amsterdam, Netherlands - April 2019; View from the Adam lookout on a bright spring day.
Amsterdam, Netherlands - April 2019; Royal Palace, Dam Square.
Amsterdam, Netherlands - April 2019; Mind the cats of the poezenboot, floating cat sanctuary on the Singel canal.
Amsterdam, Netherlands - April 2019: Bright pink tulips in the Vondelpark on a spring afternoon.
Amsterdam, Netherlands - April 2019; People at the top of the the Adam lookout on a bright spring day.
Amsterdam, Netherlands - April 2019; Bike storage at Centraal Station.
Amsterdam, Netherlands - April 2019; Museumplein on a bright spring day.
Amsterdam, Netherlands - April 2019; The Eye film museum, Noord district.
Amsterdam, Netherlands - April 2019; GVB passenger ferry.
Amsterdam, Netherlands - April 2019; At the top of the A'Dam lookout.
Amsterdam, Netherlands - April 2019; Museumplein on a bright spring day. Van Gogh museum.
Amsterdam, Netherlands - April 2019; Museumplein and the Concert Hall on a bright spring day.
Amsterdam, Netherlands - April 2019; Madam Tussaud, Dam Square.
Amsterdam, Netherlands - April 2019: Boats, canals, bikes and tall buildings.
Amsterdam, Netherlands - April 2019: People and tulips at the Rijksmuseum.
Amsterdam, Netherlands - April 2019: Dam Square.
Amsterdam, Netherlands - April 2019: Young man relaxing in the street.
Amsterdam, Netherlands - April 2019: Bikes parked on a canal bridge.
Amsterdam, Netherlands - April 2019: Lots of people in Dam Square.
Amsterdam, Netherlands - April 2019: People relaxing at museumplein.
Amsterdam, Netherlands - April 2019: Floating flower market and floating bike park on the Singel canal.
Container ship MSC Tina at the Port of Felixstowe. 29th March 2019.
Cargo Ro Ro vessel the Somerset leaving Felixstowe for Rotterdam. Port of Felixstowe, Suffolk, UK. 29th March 2019.
Working cranes at the Port of Felixstowe, Suffolk, UK. 29th March 2019.
Big Brother is watching you. CCTV camera high above the rooftops in Ipswich, Suffolk, UK. February 2019.
UK Weather: A very warm sunny February afternoon in Christchurch Park, Ipswich, Suffolk.
Designated public place. Woodbridge, Suffolk, UK. January 2019.
The Tide Mill and the River Deben at low tide. Woodbridge, Suffolk, January 2019.
A guide to Woodbridge town. Woodbridge, Suffolk, UK, January 2019.
Woodbridge art club sign and disabled parking. Suffolk, UK. January 2019.
Station guest house and whistlestop cafe over the bridge. Notice by the railway station. Woodbridge, Suffolk, UK. January 2019.
Winter trees and snow on the ground at Kyson Point. Woodbridge, Suffolk, UK, January 2019.
Disabled parking symbol painted on the ground. Tesco car park. Suffolk, UK.
Live bus times - hi tech public transport in Suffolk, UK. December 2018.
Trolley storage at Tesco supermarket. Suffolk, UK, December 2018.
2nd stop charity shop closed on Boxing Day, December 2018. Kesgrave, Ipswich, Suffolk, UK.
Supermarket trolleys outside a Tesco supermarket which is closed, Boxing Day. December 2018.
Kesgrave town council sign. Suffolk, UK. December 2018.
End of cycle path. Suffolk, UK, December 2018.
Home baking - making black treacle flapjacks.


Matt is an experienced television director who's credits include, 'The Bill', 'Byker Grove' and 'Doctors'. We're pleased to welcome him to our team here at InspirEd.
"I do teaching and guest lecturing, mainly to young adults…inspiring them, and giving practical hands on advice about how to get their first job in the industry, and what to expect when they get there".
'Matt Bloom's get into media workshop' is a fun and informative way to learn about how he made it into TV. The workshop starts with an informal talk about Matt's experiences in the TV industry and why he thinks it's important to follow our dreams and aspirations. A camera and lighting session follows as Matt demonstrates his skills with help from the students.
This is a unique opportunity for young people interested media to get first hand advice from someone who's 'been there and done that'. This workshop is perfect for students who have genuine aspirations to work in the industry.
"A while ago I was chosen to be mentored by Romeo+Juliet and Strictly ballroom director Baz Luhrmann. What a sport…driving up to Manchester to help make a short film starring Brenda Blethyn & Emma Bunton, that was shown before the Royal Premiere of Moulin Rouge, in Leicester Square. We spent a couple of days talking about film and with his guidance I made the slapstick comedy short, with Emma and Brenda kindly giving up their time for free and trekking to the North for the one-day shoot".
Matt's skills are critically acclaimed with some of his work making the finals of The Raindance Film Festival and in 2003 his film 'On The Out' won the Royal Television Society award for best drama. For a full list of his achievements please click here.
Matt is passionate about inspiring others and helping them to choose the right path when it comes to media.
For more details or to book a workshop with Matt, please get in touch.


NEW:Solros Bukett small bowl on foot 4.5 'h and 6" wide. This adorable bowl is perfect to serve candy, nuts or any other snacks from. It is handmade and hand painted after Lena A..Linderholm's design. The bowl is dishwasher safe and contains no led. Made in Portugal.


The Grand Tour takes you all around the Apostle Islands in the areas with the most picturesque sandstone formations and the most hidden beaches. This tour stops on islands (weather dependent) to let you explore the trails or relax on the boat or the beach. It really depends on where the wind is blowing from as where we go but ideally we get you to the Sand Island and Devils islands sea caves. If that is not an option we can usually get you out to the Madeline Island and Stockton Island Sea Caves.