text:
MSB expand Allerton Road office to support specialist property team
MSB have significantly expanded our South Liverpool headquarters, taking on more than 1000 sq feet of additional back office space on Allerton Road.
The extra space entails the first and second floor at 74 Allerton Road, directly opposite our flagship office, and will be devoted entirely to our growing residential property department. Four fee earners and four support staff have already occupied the new space, with room for up to six further staff.
In a restructuring, conveyancing executive Craig Passey will head the off plan purchase team and a new team has been created specifically to deal with the increasing new build residential housing market. Partner Tracey Quirk will oversee the development of the department and will be responsible for handling more complex or high value matters.
Managing partner Paul Bibby said: "The expansion comes further to our city centre office move to 4 St Paul's Square and is further linked with being able to house and support our growing team.
"Staff numbers at MSB have risen by around 30 per cent in the last 12 months. This growth hasn't been part of a deliberate recruitment drive, rather organic growth in order to meet the demands of a spike in case work. It's great to see and long may it continue."
The Allerton Road unit will also house MSBs cloud-based file archiving system meaning that the firm will be able to dispense with off site paper storage in due course.


By International Furniture Direct. Featuring 100% solid pine with a Multi-step lacquer finish. Mortise and tenon joinery construction and the Hand-forged iron pulls are custom crafted. The 100% solid wood drawers feature English dovetail joinery on drawer fronts and Metal ball bearing glides on all drawers. Special Order.


These are the criteria, values, and guidelines that Ignite Athens will try to follow. Without them, the core things we care about, it would likely become just another conference/event.
Anyone who wants to can participate. As long as you have something interesting to share and stick to the format (20 slides for 15 seconds each), you re good. If you don t like something about the way Ignite works and want to change it, let us know. We ll listen to you. But we re not going to accommodate every request for change. If that s not okay with you, you should probably find another event to attend or present at. We re not the perfect match for everyone.
When and if it becomes necessary to pick and choose who gets to present, we want to judge ideas and presentations on how interesting, innovative and sustainable they are, and not much else. That is, we want to avoid any other subjective criteria, like how well an idea fits with the theme or feel of the event, or of Athens itself. Putting up subjective barriers, even if couched in a positive way (like trying to focus the presentations) is a surefire way to kill this event. We, the organizers, are not smart enough to know what will end up being interesting and valuable to people who attend and what will not. If your idea fits the format, and is interesting to at least one person (e.g., you), you re in. If we get more people who want to present than we can handle, well, we ll figure out a fair way to deal with that problem.
This isn t your father s tech event. All presentations will be given equal standing/billing. Let them differentiate themselves on their own merit and interestingness. We don t want to have featured keynotes. Is there someone/some company that you really want to see present at Ignite Athens? Great! Get them to present. Nothing in your way.
We want Ignite to be about promoting and sharing burning innovative ideas. If those ideas happen to take the form of the company you work for, the startup you re trying to get funded, the Spin-off you are trying to leverage or any other self-serving commercial interest, then so be it. We re fine with it, really. But whatever you present had better be interesting, because that s what it s going to be judged on in people s minds. if you re going to market to people, to pitch, you d better be smart about it. Because if you re not, it s likely to blow up in your face, and end up making people hate you. Don t say we never warned you.
I guess there have to be some real rules in here, somewhere. So, here they are. Don t do anything that s going to get you or us in trouble. In short, I guess the only real rule is Don t Be Stupid. That seems to cover pretty much everything, doesn t it?


The Japan Club does a yearly event called "Cool Japan." It is hosted by the Japan Club, the Chinese Club, the Geek Club, and the Asian Studies Graduate Student Association. They wanted a design that was "kawaii" or cute illustrations that had Japanese culture items. The front of the flyer is an illustration of Japan with little illustrations of Japanese traditions and landmarks. The back shows the name of the event in a a "cool" color and a cat depicted in an Asian style. The floor sticker shows a mix of the front and back of the flyer. The poster is used for the day of the event where to food will be and features a cat eating Japanese sushi.


Blueface turns out to be one cool customer at Nardwuar demonstrates his knowledge, either due to the fact that he hasn't made much effort to hide his past or the gigantic blunt he lights up at the beginning of the interview. He actually flips the script on Nardwuar, nabbing his microphone and demanding a demonstration of the bust down dance from the journalist, who seems flustered but game, insisting that he doesn't have the same rhythmic chops as the face-tatted rapper. If anything, Blueface seems more amused by Nardwuar's gifts than amazed — a quality that seems to stem from his lack of a turntable to play records from Game, WC, Suga Free, and cassette player for a Cash Money Big Tymers tape.
The interview is hilariously awkward and a great look at Blueface's personality, which plays well off Nardwuar's enthusiastic demeanor. Bop!


text:
Martin Scorsese: "I will never have the chance to meet such an extraordinary man as Bertrand Tavernier again"
by admin	| Posted on March 29, 2021
In a text published by the Lumière Institute, the American filmmaker wished to pay tribute to his friend, the French director who died on March 25.
"Bertrand was a unique filmmaker, unlike any other". In a letter published on the website of the Lumière Institute, Martin Scorsese paid a final tribute to Bertrand Tavernier. The American filmmaker had been warned of the disappearance of his French counterpart by Thierry Frémaux. " A few hours later, he sent us this text», Explained the general delegate of the Cannes festival and president of the association Frères Lumière in the preamble of the writing.
Read also :Bertrand Tavernier, the godfather of French cinema
Scorsese and Tavernier, it is first of all the meeting of two enthusiasts of the seventh art at the beginning of the 1970s. "They (with his partner Pierre Rissient, Editor's note) had seen Mean Streets and vigorously defended it publicly. Support that meant a lot to me», Remembers the director of Taxi Driver.
"Bertrand was intimately familiar with all aspects of French cinema. It is an incredible chance for all of us that Bertrand shared his knowledge and his passion in his documentary Vjourney through French cinema, a work of great beauty», He continues between two memories, also considering that Tavernier's dictionary, 50 years of American cinema co-written with Jean-Pierre Coursodon, deserves a translation into the language of Shakespeare.
Today it is very difficult for me to tell myself that I will never have the chance to receive all this incredible energy again
"Bertrand was so passionate that he could literally knock you out. He would sit, for hours and hours, arguing for or against a film, a filmmaker, a musician, a book or a political decision. After a while, overwhelmed, you just wondered: where does all this energy come from? Today it is very difficult for me to tell myself that I will never have the chance to receive all this incredible energy again. That I will never have the chance to meet such an extraordinary man, such an irreplaceable man», Concludes Scorsese, visibly very touched by the disappearance of his friend.
EntertainmentBertrand tavernier box office Cinema Martin scorsese Movie movies poster rooms trailers
Annie Girardot, open hearted
Scream: five anecdotes from twenty-five years of hemoglobin
Mulan on Disney +: portrait of a young girl on fire

redpajama_set_name: RedPajamaCommonCrawl


Du, F., Plaisant, C., Spring, N., Shneiderman, B.
People often seek examples of similar individuals to guide their own life choices. For example, students making academic plans refer to friends; patients refer to acquaintances with similar conditions, physicians mention past cases seen in their practice. How would they want to search for similar people in databases? We discuss the challenge of finding similar people to guide life choices and report on a need analysis based on 13 interviews. Our PeerFinder prototype enables users to find records that are similar to a seed record, using both record attributes and temporal events found in the records. A user study with 18 participants and four experts shows that users are more engaged and more confident about the value of the results to provide useful evidence to guide life choices when provided with more control over the search process and more context for the results, even at the cost of added complexity.


Published on May 8, 2010 in Complaints, Elections, General T&T, Law, Letters, PNM, Politics and UNC. 4 Comments Tags: Herbert Volney, Judiciary, Martin Daly, Politics, Raffique Shah, T&T Govt.
Let us look at the two words swift descent. Swift according to Funk and Wagnall's' dictionary means "quick," while descent means "a decline or deterioration." Is Martin Daly suggesting that former Judge Volney's decision to enter politics is a quick descent into an arena which is of lower status? One would assume that Daly is aware that it is politicians, with the aid of members of the legal profession, who draft laws, debate them, and then vote on them before they become laws of the country. The judiciary is then assigned the tasks of implementing these said laws. Martin, what do you mean by swift descent?
Secondly, I question part of the statement "inevitably raises a concern in people's minds …" For example: Is it some persons? Is it the majority? What exactly do you mean? Are you insinuating that all "Tringonians" would stereotype the entire judiciary -all judges would aspire to become politicians – because of the actions of one judge? Hello! Give us some credit, we are lot smarter than that.
Judge Volney is the first to resign from the bench in Trinidad and Tobago to run for a political post, but this is not unique in the western hemisphere. For example, in the US, Judge Brian Sandoval resigned from the Bench in 2009 to run for governor of Nevada (2010 election). Shah claimed that if Judge Volney had "allowed six months between leaving the Bench and running for election, no one would have questioned his independence." Raffique, do you have any information/data that would lead you to conclude that "no one would have questioned his independence?" That is a very, very bold statement. Are you clairvoyant?
Manning called a snap election. Volney did not have much time to ponder whether he should run or not, when election is about one month away. Taking a day to make such a decision may be deemed too short a time for some, but who knows what Volney had to go through that day. Was it a "long day" for him? According to Ramesh Deosaran, May 2, 2010, "Volney himself, more than any other judge in [the] history [of Trinidad and Tobago], has made one complaint after another, one criticism after another about how the 'criminal justice system has collapsed,' [and] about how the jury system has 'grown perverse.'" I concur with Deosaran when he suggested that "Volney no doubt feel, given [his] expressed frustration with the judiciary, that [he] can put things right as [an] elected politician." Was Volney's decision to run for political office akin to crossing the Rubicon?
Raffique Shah stated that "today a judge [in Trinidad and Tobago has resigned], tomorrow a police chief … where do we draw the line?" Again, in Trinidad and Tobago people who are members of various professions who resign their job to run for a political post may be viewed as weird by some, but the internet has informed me that such action by professionals is not that rare in some western societies. Here is an example: Fire Chief, Danny Leeper, had to resign from an $81,625.00 per year salary and a 26-year career with the Fernandina Beach Fire Department, Florida, to run for District 1 seat of the Nassau County Commission (searchamelia.com2008/07/23). Do we need to ask the question regarding drawing the line, as Shah suggested, or do we need to search the "net" and professional journals for information on political issues, for example, and then make informed statements? Volney's move did not make me queasy, instead, and please excuse me for being redundant, it encouraged me to research this political issues.
Mr Phd PETE HALL, of Edmonton, Alberta Canada , you can afford to slice and dice this desperate ,imbecilic, Kamla political Volney maneuver as best as you can, but it just won't stand up to public scrutiny.
Even the good lady , at least had the decency to reluctantly admit that -even if legal -it was an ill-conceived move, that opened the Judiciary to question as to bias and independence.
What I however find most reprehensible ,is that characters like yourself would proudly tout the merits, politically maturity , and sophistication of your new great Northern homelands such as Canada. Be it known ,that never would this despicable type of behavior be tolerated in any of these modern democratic countries.
Yet , you feel compel to defend same , in a country like ours, and why? Some -not me- would suspect that you are lobbying for a job , with this subtle , though indirect fronting of your credentials.
Phd indeed , how vain! A two bit second rate , incompetent , disgruntled judge resigned his office on Monday , and is ready to run for political office by Tuesday and Dr. Pete Hall , sees no impropriety in that.
Lord please save us from dishonest , and morally unsound folks like these that profess to be loving patriots.
Neal, Neal, Neal. Climb out of your cave and abandon your partisan politics.Your ramblings are so skewed that I am wondering if you are cokey-eyed.
Not only is volney's candidacy going to " stand up to public scrutiny", but he is going to win the election in St.Joseph.
hi all, I see Neal is still at it, but maybe he knows all right.
@ Neil….Mr. Volney's choice to move into the political arena should be commended for having the balls to walk the talk…unlike alot of brilliant minds back home who seem to have forgotten the findings in the Mustill Report….this has turn out to be a political move that has frozen the PM/AG in their track….So Neil when the PM/AG tried to lockup the CJ on a Friday night, what were your responce?


I am Derek Edwards, designer and director of Patwa, a creative studio based in Spike Island, Bristol. I have 20 years experience spanning brand identity, packaging, exhibition, brochures, publications, illustration and digital. My emphasis is on creating meaningful design that people feel connected to.
I work for start-ups, local enterprises and globally known brands, in a variety of areas including; arts, culture, community, health, food, music and corporate sectors.
Before setting up Patwa in 2005 I was employed as a designer at Realworld Records, Womad Festivals, Financial Times, Mediator Communications and Fresh Britain.


text:
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  const output = <%= camelAppName %>(flags)
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})()

redpajama_set_name: RedPajamaGithub


text:
University Archives Holdings Engineering Physics Department John Bardeen Papers
John Bardeen Papers, 1910-91 | University of Illinois Archives
Title: John Bardeen Papers, 1910-91
Series Number: 11/10/20
Acquired: 12/22/64; 2/8/65; 2/19/74; 9/10/91; 12/20/91; 5/19/92;9/27/2002; 5/14/2003, 5/23/07
by type of material and chronologically thereunder
Bardeen, John (1908-1991)
May 23, 1908 Born, Madison, Wisconsin - son of Medical School Dean Charles R. Bardeen and Althea Harmer
1923 University High and Madison Central
1928 B.S. Wisconsin, Electrical Engineering. Western Electric. Edward Bennett electrodynamics course
1929 M.S. Wisconsin, Electrical Engineering. J. H. Van Vleck
1930-33 Geophysicists, Gulf Research and Development Corporation - Leo J. Peters
1936 Ph.D. Princeton, Mathematics and Physics. E. P. Wigner
1935-38 Junior Fellow, Harvard. J. H. Van Vleck and P. W. Bridgman
1938 Married Jane Maxwell, July 18
1939-41 Assistant Professor of Physics, University of Minnesota
1941-45 Principal Physicist, U. S. Naval Ordnance Laboratory (Washington D.C.), magnetism and minesweeping
1945-51 Radar, Bell Telephone Laboratories, Murray Hill, New Jersey
1947 Surface physics - properties of contacts between metals of semi-conductors depend on electron behavior in surface energy levels
1948 Transistors with William B. Shockley and Walter Brattain. Point contact type
1951-75 Professor of Physics and Electrical Engineering, University of Illinois. Louis Ridenour. Xerox Corporation
1952 Transistor literature
March 1953 Fortune article on transistors
Theoretical understanding of semiconductors
Explained why metal-germanium contact gives effect of a PN junction in a emiconductor - in terms of surface energy states
1956 National Academy of Science member
1957 Theory of Superconductivity - vanishing of electrical resistance of some metals at low temperatures
1975 Professor Emeritus, University of Illinois
January 30, 1991 Died, Boston, Massachusetts
Electrical conduction in semiconductors and metals; surface properties of semiconductors; theory of superconductivity; and diffusion of atoms in solids
Prizes and Honors:
1952 Ballantine Medal, Franklin Institute
1954 Buckley Prize (solid state)
1955 Scott Medal, D.Sc. Union
1956 Nobel Prize for Physics
1960 D.Sc. University of Wisconsin
1962 London Prize
1974 D.Sc. University of Illinois
1977 Presidential Medal of Freedom
1987 Lomonosov Award, Soviet Academy of Sciences
1990 John Bardeen Chair in Physics and Electrical and Computer Engineering Established at University of Illinois
Bell Laboratories
Many Body Theory
Naval Ordnance Laboratory
Union of Soviet Socialist Republics - Science and Technology
University of Illinois Center for Advanced Study
Xerography
John Bardeen Papers, Honorary Degree, 1960
146 Library, 1408 W. Gregory Drive
Archives Research Center, 1707 S. Orchard Street
Digital files: May require 24 hours notice for retrieval
Filter by box:
146 Library, 1408 W. Gregory Drive 001-15
146 Library, 1408 W. Gregory Drive 088-123
146 Library, 1408 W. Gregory Drive 132
Archives Research Center, 1707 S. Orchard Street Artifact
Digital files: May require 24 hours notice for retrieval e-records
146 Library, 1408 W. Gregory Drive OS 30
Description: Papers of John Bardeen (1908-91), Professor of Electrical Engineering and Physics (1951-91), include publications (1930-91), dissertations and theses by Bardeen students (1952-85), correspondence (1945-70, 1972-91) and reports, certificates and diplomas for honorary degrees and honors, preprints, reprints, drafts, speeches, notes, photographs, slides, tape recorded interviews, & motion pictures (1910-91) concerning theoretical physics, solid state physics, development of the transistor, xerography, development of the theory of superconductivity with J. R. Schrieffer & Leon Cooper, Bardeen's 1956 and 1972 Nobel prizes and lectures, the applications of transistors & superconductivity, many body theory, Russian & Chinese physics research, American Physical Society, Army Research Office, Bell Laboratories & the transistor (1936-68), Center for Advanced Study, International Union of Pure and Applied Physics, Naval Ordnance Laboratory, Navy Research Office, Sony Corporation, Supertex, Inc., White House Science Council (1979-83), Xerox Corporation, electrolytic transistor invention patents (1910-60), consulting work, and the Physics Department. Correspondents include Walter H. Brattain, P. W. Bridgman, Leon Cooper, Harry G. Drickamer, H. Frohlich, Nick Holonyak, Jr. (BS '50, MS '51, PhD '54), Charles Kittel, J. R. Schrieffer (MS '54, PhD '57), Karlheinz Seeger, Frederick Seitz, William B. Shockley, & J. H. Van Vleck. Tape recorded interviews contain comments on theoretical physicists, experimental physics, theory of superconductivity, many body theory, scientific publication, education, Dr. Charles Bardeen, solid state physics, considerations in coming to Illinois, recruitment of new faculty, scientific understanding & communication between & education of non-scientists & scientists. Motion pictures include a May, 1972 interview with Bardeen, and a December, 1972 Swedish film on the contributions of Bardeen, Cooper, and Schrieffer to the theory of superconductivity. Papers also contain Illiniwek cap.
On-line Images / Records
Documents and Files:
John Bardeen Papers (Digital Surrogates)
Series 2: PUBLICATIONS
Series 3: DISSERTATIONS (produced by Bardeen students)
Series 5: SUBJECT FILE
Series 6: MATERIALS FROM LILLIAN HODDESON
Series 7: CERTIFICATES, CITATIONS, AWARDS, AND HONORARY DEGREES


text:
\section{INTRODUCTION}
Networks are extensively used in many real-world scenarios to represent various objects and their relationships, such as social networks\cite{b01,b02}, protein networks\cite{b03,b04}, knowledge graphs\cite{b05,b06} and citation networks\cite{b07}. Because it is difficult to carry out network analysis tasks directly on huge and complicated networks, network embedding is proposed to embed nodes into meaningful low-dimensional vectors in the euclidean space which capture intrinsic features of networks so that many downstream tasks, such as node classification\cite{b08,b09} and link prediction\cite{b10,b11,xu,xu2} can benefit from them (e.g., taking them as input to improve many downstream tasks).

In fact, real-world networks often compose of several types of nodes and edges (relations) with rich semantics, widely known as heterogeneous information network (HIN). Taking the extracted bibliography data from DBLP\footnote[1]{https://dblp.uni-trier.de/db/} in Fig.~\ref{fig1}(a) as an example, it consists of four types of nodes (i.e., paper, conference, term and author) and three types of edges (i.e., write/written relation, publish/published relation and contain/contained relation), which represent three different relations related to paper.

Opposite to the homogeneous network with a single type of nodes as well as a single type of edges, nodes in HIN have diverse type properties and different relation patterns among them. Obviously these additional information need to be considered when extracting features from HINs.
While there are already many network embedding approaches for homogeneous networks (e.g., factorization-based\cite{b12,b13}, random-walk-based\cite{b14,b15} and graph-neural-network-based\cite{b16,b08} methods), almost all these approaches have problems when facing HINs because they treat all nodes equally and ignore the influence of different relational semantics.

So far, considerable research work has implemented heterogeneous network embedding with the help of definitions of meta-paths, such as metapath2vec\cite{b17} and ESim\cite{b18}. On this basis, some others introduce graph neural network (GNN) to extract highly expressive features\cite{b19,b20,b21,b22}. Besides, of all GNN methods, graph convolutional networks (GCNs)\cite{b08} can effectively model structural dependencies. Some methods, such as GraphInception\cite{b23} and DHNE\cite{b24}, decompose HINs and adopt GCN to learn with obtained sub-networks separately. However, existing work more or less has the following shortcomings. Firstly, manual definitions of meta-paths in different HINs depend on specific domain knowledge and cause high costs. Secondly, models extract the features inside each type of semantic relations and then ignore the mutual effects between different types of nodes and relations, leading to suboptimal performance. Finally, node representations are just acquired from their related relational dependencies, causing to lose some aspects of information from global networks.

In order to address the above-mentioned issues, we propose a novel Graph-Aggregated Heterogeneous Network Embedding, named GAHNE. As Fig.~\ref{fig1}(b) shows, GAHNE employs node-level learning, channel aggregation and global feature fusion to generate node embeddings. First of all, according to the types of relations (edges), GAHNE divides the whole HIN into several single-type sub-networks, in which only one unique type of relations (edges) exists, which can remove the requirement of defining meta-paths. Since then, GAHNE obtains different semantic representations of nodes from internal channels by applying highly efficient and flexible graph convolution on each single-type sub-network.
\begin{figure*}[tbp]
\setlength{\belowcaptionskip}{-0.5cm}
\centering
\includegraphics[width=0.9\textwidth]{fig2.eps}
\captionsetup{font={footnotesize}}
\caption{Overview of our work. (a) An illustrative example of a heterogeneous network (DBLP). (b) The overall architecture of GAHNE. Given an example of HIN which has three types of relations (color indicates the relation type). GAHNE firstly divides the HIN into single-type sub-networks and then aggregates the embeddings of nodes obtained in all sub-networks. Finally, GAHNE fuses the global information from the whole HIN (colourless network).}
\label{fig1}
\end{figure*}
After that, we introduce three aggregation mechanisms in GAHNE to adaptively integrate the representations from all channels in order to obtain the optimal combination of them. As a result, the mutual effects between different types of nodes and relations can be considered, which is helpful for completing feature representation comprehensively. Furthermore, for the sake of preserving the information from global networks, GAHNE fuses extra features obtained by graph convolution on the whole network to generate final node embeddings. Finally, the proposed model can be trained by back-propagation in an end-to-end manner. Our contributions are as follows:
\begin{enumerate}
\item We propose a novel Graph-Aggregated Heterogeneous Network Embedding (GAHNE) based on graph convolutional neural networks, which aims to extract the semantics of HINs as comprehensively as possible to improve the results of downstream tasks.
\item We elaborately design channel aggregation module with three candidate aggregators to aggregate semantic representations generated from different single-type sub-networks and global feature fusion module to fuse the global information into final embeddings.
\item We compare GAHNE with many state-of-the-art baselines on three real-world HIN datasets for semi-supervised node classification and unsupervised node clustering to show the effectiveness of node embeddings learned by GAHNE. Further analysis and visualization intuitively reveal the superiority of proposed model.
\end{enumerate}
\setlength{\parskip}{0.3em}
\section{THE PROPOSED MODEL}
\setlength{\parskip}{0em}
Before detailed introduction, we give some necessary definitions first. In a heterogeneous information network, denoted as $ \mathcal{G}=(\mathcal{V},\mathcal{E},\phi,\psi)$, where $\mathcal{V}$ indicates the object set and $\mathcal{E}$ indicates the link set. A heterogeneous graph is also associated with a node type mapping function $\phi:\mathcal{V}\to\mathcal{A}$ and a link type mapping function $\psi:\mathcal{E}\to\mathcal{R}$. $\mathcal{A}$ and $\mathcal{R}$ denote the sets of predefined object types and link types, where $\left|\mathcal{A}\right|+\left|\mathcal{R}\right|>2$. In this part, we describe our novel Graph-Aggregated Heterogeneous Network Embedding (GAHNE), as illustrated in Fig.~\ref{fig1}(b), which includes three parts: node-level learning, channel aggregation and global feature fusion.

\subsection{Node-Level Learning}
As mentioned in the previous section, HINs include several types of nodes and edges (relations). In order to obtain the feature embedding of a target node (i.e., node embedding), we should consider the influence of all kinds of relational semantics comprehensively. Hence, we process each of them independently at first. More specifically, we split the integral HIN $\mathcal{G}$ with $T$ types of relations into a series of sub-networks, denoted as $\{\mathcal{G}_t\mid t=1,2,\cdots,T\}$. As demonstrated in Fig.~\ref{fig1}(b), there are three single-type networks derived from the initial HIN. After this, we separately convolve and learn the deep semantic meanings of nodes in each obtained network via graph convolution operation. Kipf et al.\cite{b08} proposed the spectral graph convolution in the fourier domain basing on normalized graph laplacian $L^{sym}=I-D^{-\frac{1}{2}}AD^{-\frac{1}{2}}$, where $I$ denotes the identity matrix, $A$ denotes the adjacency matrix and $D = diag(\sum_{i}A(i,j))$. Corresponding to our problem, we can acquire the different relational semantics representations of a target node by multi-channel networks which respectively apply graph convolutions on these sub-networks.

To be specific, let $\{A_t\mid t=1,2,\cdots,T\}$ mean the adjacency matrices related to $\{\mathcal{G}_t\}$ with $N_t$ nodes $v^{i}_{t}\in\mathcal{V}_t$, edges $(v^{i}_{t},v^{j}_{t})\in\mathcal{E}_t$ and $D_{t} = diag(\sum_{i}A_t(i,j))$. Further, we use $P_{t}=D_{t}^{-1}A_t$, derived from random walk normalized laplacian instead of $L_{t}^{sym}$, since an asymmetric matrix is more suitable to define the fourier domain in view of the varied degree distribution in a HIN and the directed interaction between two connected nodes\cite{b24}. Moreover, compute the eigendecomposition: $P_{t} = U_{t} \Lambda_{t} U_{t}^{-1}$, where $\Lambda_{t}$ and $U_{t}$ denote the eigenvector matrix and the diagonal matrix of eigenvalues. In this way, spectral convolution on each single-relation network is defined as follows:
\begin{equation}
g_{\theta_{t}} \star X_{t} = U_{t}g_{\theta_{t}}U_{t}^{-1}X_{t} = U_{t}g_{\theta_{t}}(\Lambda_{t})U_{t}^{-1}X_{t}.
\end{equation}
The convolution is the multiplication of the input signal $X_{t} \in \mathbb{R}^{N_{t} \times D}$ of the network $\mathcal{G}_t$, where $D$ represents the input feature dimension, with a filter $g_{\theta_{t}} = diag(\theta_{t})$ parameterized by $\theta_{t} \in \mathbb{R}^{N_{t}}$, which can be understood as a function of the eigenvalues of $P_{t}$ (i.e., $g_{\theta_t}(\Lambda_t)$). $U^{-1}_{t}X_{t}$ is the graph fourier transform of input signal. Defferrard et al.\cite{b26} proposed that $K^{th}$-order polynomial Chebyshev polynomials can well-approximate $g_{\theta_{t}}$ as follows:

\begin{equation}
g_{\theta_{t}}(\Lambda_{t}) = \sum_{k=1}^{K}\theta_{t}^{k}\Lambda_{t}^{k},
\end{equation}
where $\theta_{t} \in \mathbb{R}^{K}$ is now a vector of Chebyshev coefficients. From Eq. 2 we have:
\begin{equation}\label{con:eq3}
g_{\theta_{t}} \star X_{t} = U_{t}(\sum_{k=1}^{K}\theta_{t}^{k}\Lambda_{t}^{k})U_{t}^{-1}X_{t} = \sum_{k=1}^{K}\theta_{t}^{k}P_{t}^{k}X_{t},
\end{equation}
\begin{equation}
H_{t} = \sigma(\sum_{k=1}^{K}P_{t}^{k}X_{t}\Theta_{t}),
\end{equation}
\setlength{\parskip}{-0.3em}
where $\Theta_{t} \in \mathbb{R}^{D\times d}$ denotes the trainable weight matrix which maps the input feature dimension $D$ to $d$ shared over the whole network $\mathcal{G}_t$ and $H_{t} \in \mathbb{R}^{N_{t}\times d}$ indicates the output matrix. We use an activation function $ReLU(\cdot)$ to add nonlinearity to output signals.
\subsection{Channel Aggregation}
In the previous section, we have learned the feature information of nodes on each sub-network. The final embeddings involve feature information in different relational scales that have unequal effects. Therefore, we need to aggregate output embeddings from all channels to obtain more comprehensive vector representations. Supposing $\{h_{t}^{v} \mid t = 1,2,\cdots,T\}$ is the set of output embeddings of a certain node $p_{v}\in \mathcal{V}$ generated from all $T$ channels in node-level learning. If a node does not belong to a sub-network, we use a zero vector to make complement. The aggregated embedding $z^{v} \in \mathbb{R}^{d}$ of $p_{v}$ can be defined as:
\setlength{\parskip}{0em}
\begin{equation}
z^{v} = Aggregation(\{h_{1}^{v}, h_{2}^{v}, h_{3}^{v}, \cdots, h_{T}^{v}\}),
\end{equation}
where $Aggregation(\cdot)$ denotes the special aggregator to carry out this purpose. Fig.~\ref{fig1}(b) cites a simple instance of this process. Here we introduce three choices of qualified candidate aggregators:
\begin{itemize}
\item \textbf{Attention-based Aggregator.} Inspired by some researches on attention mechanism in convolutional neural network (CNN)\cite{b33}, we propose a novel channel attention mechanism to measure the significance of feature information from every channel. This mechanism first makes non-linear transformation for the channel-wise embedding $h_{t}^{v}$ and then multiplies transformation result by a parameterized attention vector $q \in \mathbb{R}^{d_{q}}$ to get the importance of specific channel-wise embedding, where $d_{q}$ is the dimension of attention vector. Afterwards, we can calculate the sum of all the importance and then acquire the numerical value $w_{t}$ that represents the importance of sub-network $t$ in the final embedding. The formula of $w_{t}$ is as follows:

\begin{equation}
w_{t} = \sum_{v \in \mathcal{V}}q^{\mathsf{T}}\cdot Tanh(W\cdot h_{t}^{v} + b),
\end{equation}
where $W \in \mathbb{R}^{d_{q} \times d}$ and $b \in \mathbb{R}^{d_{q}}$ are non-linear transformation parameters. Next, the weight of each sub-network $\mu_{t}$ can be deduced by a softmax normalization as follows:
\begin{equation}
\mu_{t} = \frac{exp(w_{i})}{\sum_{i=1}^{T}exp(w_{i})}\label{eq5}.
\end{equation}
In particular, for $\forall t \in \{1, 2, \cdots, T\}$, we have $\mu_{t}^{1} = \mu_{t}^{2} = \cdots = \mu_{t}^{N}$, where $N$ is the number of nodes and $\mu_{t}^{v}$ denotes the weight of channel $t$ for node $v$.
In this way, we can aggregate these embeddings to the multi-channel embedding $z_{v}$ as follows:
\begin{equation}
z^{v} = \sum_{t=1}^{T}\mu^{v}_{t}h^{v}_{t}.
\end{equation}
\item \textbf{Gated Aggregator.} Gate unit\cite{b35} can effectively control the flow of previous information. Inspired by relevant thought, this mechanism proposes a gated module, which selectively aggregates rich scale features. The parametered gated vectors filter all dimensions of channel-wise embeddings:
\begin{equation}
g^{v}_{t} = Sigmoid(W'_{t}\cdot h_{t}^{v}),
\end{equation}
\begin{equation}
z^{v} = \sum_{t=1}^{T}g^{v}_{t} \odot h^{v}_{t}.
\end{equation}
Here $W'_{t} \in \mathbb{R}^{d \times d}$ is the parameterized training vector and $g^{v}_{t} \in \mathbb{R}^{d}$ indicates the gate obtained by the sigmoid function which controls the flow of information from $h^{v}_{t}$ and the scale of each component in $g^{v}_{t}$ is $[0,1]$.

\item \textbf{Pooling Aggregator.} The final aggregator adopts mean-pooling operator to aggregate all channel-wise embeddings. In this pooling approach, each channel's output of a node is input in a fully-connected layer. Following this feature processing, we apply an element-wise mean-pooling operation to aggregate information across the channel set:
\begin{equation}
z^{v} = mean(\{ReLU(W^{pool}h_{t}^{v}+b^{pool}),\forall t \in \{1, 2, \cdots, T\}),
\end{equation}
where $mean$ denotes the element-wise mean operator and $ReLU(\cdot)$ is the activation function. This approach is inspired by advancements in GraphSAGE\cite{b16}. Our aggregator firstly computes features in the channel set and then uses the mean-pooling operator to effectively capture different aspects of the channel set.
\end{itemize}

Furthermore, let $Z_t$ indicates the aggregated embeddings of nodes in $\mathcal{G}_t$ and we provide a multi-layer convolutional neural network with the following layer-wise propagation rule:
\begin{equation}
H_t^{(l+1)} = \sigma(\sum_{k=1}^{K}P_tZ_t^{(l)}\Theta^{(l)}_{t}).
\end{equation}
Here, $\Theta^{(l)}_{t}\in \mathbb{R}^{d^{(l)} \times d^{(l+1)}}$ and $H_t^{(l+1)}\in \mathbb{R}^{N \times d^{(l+1)}}$ are specific trainable weight matrix and output signals, in which $d^{(l)}$ denotes feature dimension of $l^{th}$ layer. $Z^{(l)} \in \mathbb{R}^{N \times d^{(l)}}$ is the matrix of aggregated input embeddings in the $l^{th}$ layer; $Z_t^{(0)} = X_t$.
\setlength{\parskip}{-0.3em}
\subsection{Global Feature Fusion}
High-quality feature representation requires HINs to leverage both the homologous and heterogeneous information. Although we have performed the convolutions separately on each sub-network, we also lack a global scope of structural and semantic feature representation on the whole HIN.
To solve this challenge, we design an overall fusion layer shown in Fig.~\ref{fig1}(b). The input of this layer contains aggregated sub-network embeddings after $m$ layers $Z^{(m)} \in \mathbb{R}^{N \times d^{(m)}}$ and the embeddings $Z_{w}\in \mathbb{R}^{N \times d^{(m)}}$ learned on the whole graph. We can obtain $Z_{w}$ by using traditional GCN network as usual. In this layer, we explicitly fuse $Z^{(m)}$ and $Z_{w}$ to hold both partial and overall information:
\setlength{\parskip}{0em}
\begin{equation}
E_{f} = \left[Z^{(m)}, Z_{w}\right],
\end{equation}
here $E_{f} \in \mathbb{R}^{N \times 2d^{(m)}}$ is the concatenation of these two matrices, which carries more information. Following this, we use a fully-connected layer to provide more flexibility and non-linearity to learn the fused embeddings:
\begin{equation}
E = \sigma(W^{fc}E_{f}+b^{fc}).
\end{equation}
\setlength{\parskip}{-0.3em}
Now we have the final embeddings $E \in \mathbb{R}^{N \times d^{(m)}}$ of all nodes. And $W^{fc}$, $b^{fc}$ denote the weight matrix and bias of the layer.
\subsection{Model Training}
The final embeddings we obtain can be used as the input of the downstream tasks, and then design specialized loss function. We use a fully-connected layer and a softmax activation function $\sigma$:
\begin{equation}
F = softmax(E\Theta'),
\end{equation}
\setlength{\parskip}{0em}
here supposing the target node has $C$ class and $\Theta' \in \mathbb{R}^{d^{(m)} \times C}$ denotes the dimension reduction transformation matrix and $F \in \mathbb{R}^{N \times C}$ is the final probability matrix in which $F_{ij}$ indicates the probability that the $i^{th}$ node belongs to class $j$. Therefore, we can optimize the model weights by minimizing the cross entropy between the ground-truth and the prediction over a small fraction of labeled nodes iteratively:
\begin{equation}
Loss = -\sum_{v\in \mathcal{V}_{l}}\sum_{c=1}^{C} Y^{v}\lbrack c\rbrack\cdot\ln(F^{v}\lbrack c\rbrack),
\end{equation}
where $\mathcal{V}_{l}$ is the set of labeled nodes, $Y^{v}$ is the one-hot vector indicates the ground-truth labels of nodes and $F^{v}$ are the embeddings of labeled nodes. With the guide of labeled data, we can optimize the model via back propagation and learn the embeddings of nodes.

\section{EXPERIMENTS}
\subsection{Experimental Settings}

\linespread{1}
\begin{table}[t]
\captionsetup{font={footnotesize}}
\caption{Statistics of the datasets.}
\begin{center}
\begin{tabular}{cccc}
\toprule[0.9pt]
\textbf{Statistics}&\textbf{DBLP}& \textbf{Yelp}&\textbf{MovieLens}\\
\hline
\textbf{$\sharp$ Nodes} &$37791$&$3913$&$13119$ \\
\textbf{$\sharp$ Edges} & $170794$& $38668$ & $57932$ \\
\textbf{$\sharp$ Training} & $400$($9.8\%$) & $400$($15.3\%$)& $600$($16.3\%$) \\
\textbf{$\sharp$ Validation} &$200$($4.9\%$)& $200$($7.7\%$)& $300$($8.2\%$) \\
\textbf{$\sharp$ Test} &$3457$&$2164$& $2772$ \\
\toprule[0.9pt]
\end{tabular}
\end{center}
\label{table1}
\end{table}
\begin{enumerate}[leftmargin=*]
\item \textbf{Datasets.}
\begin{table*}[tb]
\captionsetup{font={footnotesize}}
\caption{Experiment results ($\%$) for the node classification task. (bold: best, underline: runner-up)}
\begin{center}

\resizebox{0.99\textwidth}{!}{
\begin{tabular}{|c|c|c||c|c|c|c|c|c|c||c|c|c|}
\hline
Datasets&Metrics & Train$\%$&Deepwalk&HIN2Vec&metapath2vec&GAT & HAN &GCN& DHNE & \tabincell{c}{GAHNE$_{atte}$} & \tabincell{c}{GAHNE$_{gate}$}  & \tabincell{c}{GAHNE$_{pool}$}  \\
\hline

\multirow{8}{*}{DBLP}&\multirow{4}{*}{Macro-F1}&$20\%$& 87.35 & 85.12 & 89.82 & 93.53 & 92.58 & 92.31 & 92.27 & \underline{94.17} & \textbf{94.32} & 93.23\\
& & $40\%$& 88.19 & 87.48 & 89.67 & 93.42 & 92.28 & 92.25 & 92.34 & \underline{94.10} & \textbf{94.46} & 93.31\\
& & $60\%$& 88.93 & 88.98 & 89.57 & 93.58 & 92.00 & 92.67 & 92.76 & \textbf{94.63} & \underline{94.52} & 93.54\\
& & $80\%$& 89.15 & 89.81 & 88.99 & 93.35 & 92.70 & 92.52 & 92.53 & \underline{94.28} & \textbf{94.48} & 93.57\\
\cline{2-13}
&\multirow{4}{*}{Micro-F1}& $20\%$& 87.91 & 85.68 & 90.41 & 94.02 & 92.14 & 92.82 & 92.95 & \underline{94.43} & \textbf{94.72} & 93.69\\
& & $40\%$& 88.71 & 87.98 & 90.26 & 93.88 & 92.78 & 92.74 & 92.99 & \underline{94.45} & \textbf{94.83} & 93.76\\
& & $60\%$& 89.51 & 89.51 & 90.25 & 94.04 & 92.54 & 93.16 & 93.42 & \underline{94.70} & \textbf{94.96} & 93.99\\
& & $80\%$& 89.72 & 90.29 & 89.72 & 93.88 & 93.20 & 92.86 & 93.00 & \underline{94.54} & \textbf{94.77} & 93.90\\
\hline

\multirow{8}{*}{Yelp}&\multirow{4}{*}{Macro-F1}&$20\%$& 62.28 & 69.25 & 67.62 & 68.68 & 69.58 & 71.96 & 70.93 & \underline{72.25} & 72.08 & \textbf{72.96}\\
& & $40\%$& 66.11 & 70.35 & 70.02 & 70.21 & 70.33 & 73.00 & 72.18 & \underline{74.16} & 73.24 & \textbf{74.89}\\
& & $60\%$& 67.61 & 71.33 & 71.55 & 71.02 & 69.83 & 72.57 & 72.45 & \textbf{74.18} & 73.05 & \underline{74.12}\\
& & $80\%$& 67.70 & 71.51 & 72.26 & 70.82 & 70.91 & 73.80 & 71.42 & \underline{74.61} & 73.40 & \textbf{75.99}\\
\cline{2-13}
&\multirow{4}{*}{Micro-F1}& $20\%$& 72.81 & 76.06 & 74.83 & 75.31 & 75.17 & 76.85 & 76.86 & 77.25 & \underline{77.33} & \textbf{77.65}\\
& & $40\%$& 74.99 & 76.68 & 76.00 & 76.25 & 75.17 & 77.35 & 77.63 & \underline{78.69} & 78.02 & \textbf{78.85}\\
& & $60\%$& 76.16 & 77.46 & 77.29 & 76.93 & 75.38 & 77.11 & 77.61 & \underline{78.70} & 77.58 & \textbf{78.38}\\
& & $80\%$& 75.62 & 77.71 & 77.40 & 76.67 & 75.93 & 77.94 & 77.15 & \underline{78.93} & 78.05 & \textbf{79.77}\\
\hline

\multirow{8}{*}{MovieLens}&\multirow{4}{*}{Macro-F1} & $20\%$& 56.53&53.41&52.50& 58.64 &52.24& 59.61&57.67& \underline{60.89} & 60.58 & \textbf{62.39}\\
&  & $40\%$& 59.94 & 56.90 & 55.52 & 60.42 & 52.26 & 60.64 & 58.58 & \underline{62.18} & 61.73 & \textbf{62.50}\\
&  & $60\%$& 60.79 & 59.26 & 58.33 & 60.05 & 52.42 & 61.38 & 57.57 & \underline{62.28} & 61.39 & \textbf{62.68}\\
&  & $80\%$& 62.55 & 61.63 & 60.30 & 61.01 & 53.69 & 61.74 & 60.14 & 62.79 & \underline{62.85} & \textbf{64.12}\\
\cline{2-13}
&\multirow{4}{*}{Micro-F1} & $20\%$ & 58.52 & 55.23 & 54.36 & 60.38 & 54.85 & 60.88 & 58.96 & \underline{62.27} & 61.99 & \textbf{63.45}\\
& & $40\%$& 61.63 & 58.46 & 57.55 & 61.92 & 55.12 & 61.86 & 59.87 & \textbf{63.57} & 63.08 & \underline{63.55}\\
& & $60\%$& 63.22 & 60.43 & 59.76 & 61.63 & 55.45 & 62.53 & 58.88 & \underline{63.32} & 62.64 & \textbf{63.66}\\
& & $80\%$& 64.04 & 62.79 & 61.77 & 62.50 & 55.70 & 63.03 & 61.46 & \underline{64.13} & 63.95 & \textbf{65.23}\\
\hline
\end{tabular}
}
\end{center}
\label{table2}
\vspace{-0.3cm}
\end{table*}
To evaluate the effectiveness of GAHNE, we use three real-world heterogeneous network datasets consisting of an academic network datasets DBLP, a social network Yelp\footnote[2]{https://www.yelp.com/dataset/} and a movie network MovieLens\footnote[3]{https://grouplens.org/datasets/movielens/}. The statistics of three datasets are summarized in TABLE~\ref{table1}. There are four types of nodes (paper (P), conference (C), author (A), term (T)), three types of edges (P-A, P-C, P-T) in DBLP and 4057 authors are divided into four research areas as labels. Yelp has five types of nodes (user (U), service (S), business (B), star level (L), reservation (R)), four types of edges (B-U, B-S, B-L, B-R), in which 2614 labeled businesses are from three categories. What's more, MovieLens is a film review dataset containing five types of nodes (movie (M), director (D), tag (T), writer (W), user (U)) with four types of relations (M-D, M-T, M-W, M-U) and there are three distinct genres in 3672 movies as labels.
\item \textbf{Baselines.}
We compare GAHNE against the following traditional, comparable, SOTA methods which aim to generate node embedding in supervised or unsupervised ways:
\begin{itemize}
\item[-] \textbf{DeepWalk\cite{b14}.} A random-walk-based network embedding method which is designed for the homogeneous networks without considering networks' heterogeneity.

\item[-] \textbf{HIN2Vec\cite{b31}.} A heterogeneous information network embedding method via a deep neural network by considering the meta-paths.

\item[-] \textbf{metapath2vec\cite{b17}.} A metapath2vec model formalizes meta-path-based random walks and then leverages skip-gram model to perform node embeddings. We test on all meta-paths separately and report the one with the best results.

\item[-] \textbf{GAT\cite{b28}.} It is a semi-supervised neural network which considers the attention mechanism on the homogeneous graphs.

\item[-] \textbf{HAN\cite{b19}.} A heterogeneous graph neural network based on the hierarchical attention, including node-level and semantic-level attentions.

\item[-] \textbf{GCN\cite{b08}.} A scalable approach for semi-supervised learning on graph-structured data based on graph convolutional networks without considering network heterogeneity. We use random walk normalized Laplacian for better performance.

\item[-] \textbf{DHNE\cite{b24}.} A semi-supervised heterogeneous network embedding method which adopts convolutional layer on each decomposed homogeneous networks and bipartite networks and then concatenates the output vectors of each node from all networks without considering the weights of different networks.

\item[-] \textbf{GAHNE$_{atte}$.} Our proposed graph neural network with attention-based aggregator.
\item[-] \textbf{GAHNE$_{gate}$.} Our proposed graph neural network with gated aggregator.
\item[-] \textbf{GAHNE$_{pool}$.} Our proposed graph neural network with pooling aggregator.
\end{itemize}
\item \textbf{Implementation Details.} For the proposed GAHNE, we randomly initialize parameters and use Adam to optimize the model with a maximum of 200 epochs (adopting early stopping with a patience of 30). In the comparative experiments, both convolutional network branches for the whole network and the sub-networks in our framework are two layers, besides, we simply set convolution parameter $K=1$ in K-order approximation and set the dimension of the attention vector $q$ in attention-based aggregator to $128$. We leave their discussions in the following experiments. As for GNNs models, including GAT, HAN, GCN, DHNE and GAHNE, we set the learning rate to $0.01$, the dropout rate to $0.5$ and the $L2$ penalty weight decay to $0.0005$. For a fair comparison, we set the embedding dimension to $64$ for all the above algorithms. What's more, we randomly divide the labeled nodes into training set, validation set and the remaining as the testing set by certain ratios. For random-walk-based methods containing DeepWalk, metapath2vec, and HIN2Vec, we set window size to $5$, walk length to $100$, walks per node to $40$, the number of negative samples to $5$. For above methods requiring meta-paths, we define the commonly used schemes ``APA'', ``APTPA'' and ``APCPA''\cite{b19} on DBLP, ``UBSBU''\cite{b34} on Yelp, ``DMTMD'' and ``DMUMD''\cite{b24} on MovieLens to guide modeling. In practice, we implement GAHNE with Tensorflow1.13\footnote[4]{https://www.tensorflow.org/} to train model parameters and also use mini-batch gradient descent, which divides training data into several batches and updates parameters by each batch. In actual use, The label of the $i^{th}$ node can be predicted as $y^{i} = argmax(F^{i})$. Our code and data are available at website\footnote[5]{https://github.com/seanlxh/GAHNE}.
\end{enumerate}
\begin{table*}[t]
\captionsetup{font={footnotesize}}
\caption{Experiment results ($\%$) for the node clustering task. (bold: best, underline: runner-up)}
\begin{center}
\resizebox{0.95\textwidth}{!}{
\begin{tabular}{|c|c||c|c|c|c|c|c|c||c|c|c|}
\hline
Datasets&Metrics&Deepwalk&HIN2Vec&metapath2vec&GAT&HAN&GCN& DHNE & \tabincell{c}{GAHNE$_{atte}$} & \tabincell{c}{GAHNE$_{gate}$}  & \tabincell{c}{GAHNE$_{pool}$}  \\
\hline
\multirow{2}{*}{DBLP}&NMI& 78.69 & 73.06 & 75.38 & 80.59 & 72.95 & 76.88 & 76.93 & \underline{81.23} & \textbf{82.21} & 80.69\\
&ARI& 83.54 & 79.21 & 80.66 & 85.45 & 78.38 & 82.92 & 81.18 & \underline{86.15} & \textbf{87.12} & 86.41\\
\hline
\multirow{2}{*}{Yelp}&NMI& 34.08 & 35.70 & 36.03 & 33.34 & 38.93 & 39.66 & 39.08 & \underline{39.74} & \textbf{40.37} & 38.80\\
&ARI& 40.66 & 40.19 & 41.33 & 34.21 & 35.41 & 35.63 & \underline{43.08} & 42.76 & \textbf{43.77} & 41.11\\
\hline
\multirow{2}{*}{MovieLens}&NMI& 5.53 & 1.53 & 4.17 & 15.29 & 9.88 & 18.60 & 11.24 & \underline{21.14} & 17.34 &\textbf{21.82}\\
&ARI& 5.60 & 0.98 & 4.22 & 17.48 & 14.21 & 16.54 & 8.59 & \underline{20.66} & 19.42 & \textbf{22.13}\\
\hline
\end{tabular}
}
\end{center}
\label{table3}
\vspace{-0.3cm}
\end{table*}

\subsection{Node Classification}
Here we compare the effectiveness of different methods by the semi-supervised node classification task on testing set nodes via KNN classifier ($k$ = 5) with varying training proportions. In order to eliminate variance, we repeat the process for 10 times and report the averaged Micro-F1 and Macro-F1 in TABLE~\ref{table2}. As can be seen, GAHNE achieves the best and stable performance. Except GAHNE, the performances have ups and downs on the GNN-based baselines (GAT, HAN, GCN, DHNE). However, our GAHNE has around $1-3\%$ performance gain over the best baseline in general, which indicates that aggregation and fusion modules contain more comprehensive information. For random-walk-based graph embedding methods, Deepwalk has a fluctuated performance and HIN2Vec performs better than metapath2vec on Yelp and MovieLens since leveraging multiple meta-paths. But they all evidently lose to GAHNE that can utilize the heterogeneous node features via GNN architecture.
Compared to convolution methods GCN and DHNE, GAHNE has around $1-4\%$ improvement because of integrating the information from different relations appropriately and providing the global information. The performance of HAN is not entirely desired maybe because it discards all intermediate nodes along the meta-paths and just considers two end nodes. On semantically complex networks, meta-paths cannot be fully utilized in this way. It is interesting to find that some methods, such as Deepwalk, HIN2Vec, GAT and DHNE only have good performance in parts of datasets. Mainly because they rely on the importance of selected meta-paths or the specific structures in different networks. Thus they easily lead to uneven classification accuracy of the classifier, or poor generalization ability.

\subsection{Node Clustering}
Here we also use the unsupervised clustering task to evaluate the quality of obtained embeddings from various methods. We employ K-means to perform node clustering, and calculate normalized mutual information (NMI) and adjusted rand index (ARI) from clustering results to evaluate these methods. The number of clusters in K-means is the same as the number of nodes' classes. We use the same model configurations as in the node classification and repeat the process for 10 times. TABLE~\ref{table3} shows the average results. We can find that GAHNE is almost superior to other baselines on all datasets. Also, GNN-based algorithms usually achieve better performance than the traditional heterogeneous models because of the advantages of the GNN models. Traditional methods may lose efficacy, particularly in MovieLens, because of the dirty labels of movies. GNN-based baselines overall have better average performances, but they all fall behind in some cases since ignoring the heterogeneity or global information. On Yelp, almost all baselines have good results, DHNE in particular, because the semantics of Yelp is relatively simple, e.g., service (S) and reservation (R) only have two nodes and adjacent nodes have high similarity. The benefits of semantic aggregation are not significant.
\begin{figure*}[tb]
\centering
\subfigure[metapath2vec]{
\captionsetup{font={footnotesize}}
\begin{minipage}{4cm}
\centering
\includegraphics[scale=0.24]{metapath2vec.eps}
\end{minipage}
}
\subfigure[GCN]{
\captionsetup{font={footnotesize}}
\begin{minipage}{4cm}
\centering
\includegraphics[scale=0.24]{GCN.eps}
\end{minipage}
}
\subfigure[HAN]{
\captionsetup{font={footnotesize}}
\begin{minipage}{4cm}
\centering
\includegraphics[scale=0.24]{HAN.eps}
\end{minipage}
}
\subfigure[GAHNE$_{atten}$]{
\captionsetup{font={footnotesize}}
\begin{minipage}{4cm}
\centering
\includegraphics[scale=0.24]{scatter1.eps}
\end{minipage}
}
\captionsetup{font={footnotesize}}
\caption{Visualization of authors' embeddings on DBLP. Different colors signify different research areas.}
\label{fig3}
\vspace{-0.5cm}
\end{figure*}

\begin{table*}[tb]
\captionsetup{font={footnotesize}}
\caption{Experiment results ($\%$) for ablation study (node classification task with $40\%$ training set and clustering task). The best performance is highlighted in boldface.}
\begin{center}
\resizebox{0.99\textwidth}{!}{
\begin{tabular}{|c||c|c|c|c|c|c|c|c|c|c|c|c|}
\hline
\multirow{2}{*}{Variant}&\multicolumn{4}{c|}{DBLP}& \multicolumn{4}{c|}{Yelp}& \multicolumn{4}{c|}{MovieLens} \\
\cline{2-13}
&Macro-F1&Micro-F1&NMI&ARI&Macro-F1&Micro-F1&NMI&ARI&Macro-F1&Micro-F1&NMI&ARI \\
\hline
GAHNE$_{atte}$& \textbf{94.30} & \textbf{94.53} & \textbf{81.23} & \textbf{86.15} & \textbf{73.80} & \textbf{78.39} & \textbf{39.74} & \textbf{42.76} & \textbf{62.04} & \textbf{63.32} & \textbf{21.12} & \textbf{20.66} \\
\hline
GAHNE$_{w/dag}$& 93.03 & 93.53 & 77.74 & 83.61 & 73.56 & 78.09 & 39.55 & 38.36 & 61.26 & 62.45 & 18.87 & 15.84 \\
\
GAHNE$_{w/dfu}$& 93.92 & 94.33 & 79.71 & 84.78 & 70.32 & 76.13 & 39.06 & 40.63 & 56.64 & 58.07 & 11.14 & 3.42 \\
GAHNE$_{tra}$& 93.12 & 93.56 & 79.34 & 85.27 & 73.32 & 77.74 & 37.72 & 39.32 & 61.36 & 62.51 & 19.37 & 18.32 \\
GAHNE$_{sig}$ & 92.94 & 93.44 & 77.52 & 82.97 & 73.47 & 78.04 & 38.39 & 40.11 & 61.57 & 62.07 & 18.12 & 18.82 \\
\hline
\end{tabular}
}
\end{center}
\label{table4}
\vspace{-0.5cm}
\end{table*}
\setlength{\parskip}{-0.2em}
\subsection{Further Investigations}
\setlength{\parskip}{0em}
\begin{enumerate}
\item \textbf{Visualization.}
To further illustrate the effectiveness of GAHNE, we conduct a group of visualization experiments to make comparisons intuitively. We project the learned embedding into a 2-dimensional space using t-SNE. Here we illustrate the authors' embeddings in DBLP obtained by metapath2vec, GCN, HAN and GAHNE$_{atte}$ in Fig.~\ref{fig3}, in which different colors are assigned to signify different research areas. From this visualization, we can see that the visual pattern of GAHNE turns out best. More concretely, it has high intra-class similarity and the boundaries between different research areas are clearly defined. As a meta-path-based network embedding model, metapath2vec relies on single meta-path and its ability is limited, so that different nodes crowd together. Moreover, since GCN does not take heterogeneity into consideration, the boundaries between different research areas are still blurry. As for HAN, the authors in the same class are scattered in the center of the picture, because HAN discards all intermediate nodes along the meta-path leading to deviation of the results.
\item \textbf{Parameter Sensitivity Analysis.} In this section, we do sensitivity analysis to some main parameters in GAHNE and Fig.~\ref{fig4} shows the NMI and ARI curves on DBLP dataset.
\begin{figure}[tb]
\centering
\subfigure[Dimension of final embedding]{\label{fig4:1}
\captionsetup{font={footnotesize}}
\begin{minipage}[t]{0.2\textwidth}
\centering
\includegraphics[width=1.05\textwidth]{plot1.eps}
\end{minipage}
}
\subfigure[Dimension of attention vector]{\label{fig4:2}
\captionsetup{font={footnotesize}}
\begin{minipage}[t]{0.2\textwidth}
\centering
\includegraphics[width=1.05\textwidth]{plot2.eps}
\end{minipage}
}

\subfigure[Number of layers]{\label{fig4:3}
\captionsetup{font={footnotesize}}
\begin{minipage}[t]{0.2\textwidth}
\centering
\includegraphics[width=1.05\textwidth]{plot3.eps}
\end{minipage}
}
\subfigure[Convolution parameter $K$]{\label{fig4:4}
\captionsetup{font={footnotesize}}
\begin{minipage}[t]{0.2\textwidth}
\centering
\includegraphics[width=1.05\textwidth]{plot4.eps}
\end{minipage}
}
\captionsetup{font={footnotesize}}
\caption{Parameter sensitivity of GAHNE.}
\label{fig4}
\end{figure}
\begin{enumerate}
\item \textbf{Dimension of the final embedding $F$.} We first test the effect of the dimension of the final embedding $F$, shown in Fig.~\ref{fig4:1}. As dimension grows, the performance raises steadily first and then shows a continued decline. The optimal performance is obtained when dimension is 64.

\item \textbf{Dimension of attention-based attention vector $q$.} Here we test how the dimension of the attention vector $q$ influences the effectiveness of attention-based aggregator. From Fig.~\ref{fig4:2}, we can conclude that our model is not sensitive to this parameter in general and the performance reaches the peak when the dimension is set to 128.

\item \textbf{Layer number of graph convolutional networks.} We compare the outcomes in different numbers of convolutional networks layers and report them in Fig.~\ref{fig4:3}. We can draw a conclusion that two-layered structure works best. A deeper network may bring bad effects because of over-smooth.

\item \textbf{Convolution parameter $K$.} We also investigate the effect of $K-order$ approximation of localized spectral filters on networks reported in Fig.~\ref{fig4:4}. Based on the results, we can find that limiting the layer-wise convolution operation to $K = 1$ works best. The information from more than one hop away from the target node offers no income, besides, we already have a two-layer structure.
\end{enumerate}
\end{enumerate}
\setlength{\parskip}{-0.5em}
\subsection{Ablation Study}
\setlength{\parskip}{0em}
In order to prove that all components of GAHNE are valid, we employ extra experiments on various variants of GAHNE. TABLE~\ref{table4} lists the results on the three datasets. Note that the proportion of training set is $40\%$ and the hyper-parameters in all convolutional networks are consistent. We denote GAHNE$_{w/dag}$ as the reference model which switches to calculate the average across the multi-channels relation embeddings instead of employing effective aggregation mechanisms. The decline in its performance, especially in clustering task, proves that our aggregation mechanism works. GAHNE$_{w/dfu}$ is an incomplete model that excludes global fusion. There is a significant decline in performance on MovieLens, which testifies the feasibility of global feature fusion. GAHNE$_{tra}$ is the contrast model by replacing multi-channels GCNs on different relations with a traditional GCN on whole networks, so that the whole framework has two same GCN branches. It confirms that capturing network features from different semantics helps improve GAHNE in a manner. GAHNE$_{sig}$ just selects a single relation embedding from multi-channels which produces the best results. The network information it captures is not comprehensive, so it causes a decline. All above ablation experiments prove the value of key parts of the proposed model.
\section{RELATED WORK}
\subsection{Graph Neural Networks}
Graph neural networks (GNNs) are classical models widely used in recent years, which transform the complicated input graph-structure data into meaningful representations for downstream mining tasks by information passing and aggregation according to dependencies in networks. Among all GNNs, graph convolutional networks (GCNs) are thought to become a dominating solution, falling into two categories: spectral\cite{b08,b25,b26} and non-spectral\cite{b27,b28} methods. As for spectral domains, Bruna et al.\cite{b25} proposed to utilize fourier basis to perform convolution in the spectral domain. ChebNet\cite{b26} introduced that smooth filters in spectral convolutions can be well-approximated by K-order Chebyshev polynomials. Kipf et al.\cite{b08} presented a convolutional architecture via a localized first-order approximation of spectral graph convolutions which further constrains and simplifies the parameters of ChebNet\cite{b26}. On the other hand, non-spectral methods are defined directly on the graph, operating on the target node and its spatial neighbors, so as to realize the convolution operation on the graph-structure. For example, Hamilton et al.\cite{b27} proposed GraphSAGE which generated embeddings by sampling and aggregating features from nodes' local neighborhood. In addition, there are many works utilizing attention layers in neural networks, such as GAT\cite{b28}, which leverages masked self-attention to enable specifying different weights to different nodes in a neighborhood. However, these methods do not deal with diverse types of nodes and edges specifically in the process of implementation, for which they are not suitable for HINs.
\subsection{Heterogeneous Network Embedding}
So far, heterogeneous network embedding learns low-dimensional vector representations of nodes mainly from the view of meta-paths semantic information. ESim\cite{b18} accepts user-defined meta-paths as guidance to learn vertex vectors in a user-preferred embedding space. Metapath2vec\cite{b30} produces meta-path-based random walks and then utilizes a heterogeneous skip-gram model to perform node embedding. HIN2Vec\cite{b31} is designed to capture the rich semantics contained in HINs by learning latent vectors of nodes and meta-paths. Some methods extend GNNs for modeling heterogeneous networks. HAN\cite{b19} proposes a graph neural network based on the hierarchical attention, including node-level and semantic-level attentions. MAGNN\cite{b32} makes improvements by enabling each target node to extract and combine information from the meta-path instances connecting the node. Besides, GraphInception\cite{b23} and DHNE\cite{b24} convert HINs into homogeneous networks in accordance with types of nodes and edges and then adopt graph convolution to learn with obtained networks separately. Following this, models concatenate all output vectors to get the final results. Nevertheless, all of the heterogeneous graph embedding methods introduced above have at least one of the following limitations: depending on manual meta-path selection process, ignoring the mutual effects between different types of nodes and relations, or discarding information from global networks.
\setlength{\parskip}{-0.3em}
\section{CONCLUSION}
In this paper, we mainly focus on processing the complex semantic information in heterogeneous information networks and give a heterogeneous graph neural network model based on graph convolution. The proposed model first converts a HIN into series of single-type sub-networks and then captures the semantic information from them respectively. After that, we provide some novel aggregation mechanisms to integrate them effectively. In addition, we fuse the supplement information from the whole network to mine networks comprehensively and stably. Extensive experiments prove that our final proposed approach GAHNE outperforms state-of-the-art methods in different areas with consistent level of performance. In the future, we will explore other possible measures to aggregate semantics and fuse the global information in order to better capture the structural information of HINs.\\[3pt]
\textbf{Acknowledgement.} The work was supported by the National Key Research and Development Program of China (No. 2019YFB1704003), the National Nature Science Foundation of China (No. 71690231), Tsinghua BNRist. Lijie Wen is the corresponding author.

redpajama_set_name: RedPajamaArXiv


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text:
Miss. Reads: Week of June 19
Get the latest of most popular books is Mississippi and schedule of readings, signings.
Miss. Reads: Week of June 19 Get the latest of most popular books is Mississippi and schedule of readings, signings. Check out this story on clarionledger.com: http://on.thec-l.com/24Z67mS
The Clarion-Ledger, The Clarion-Ledger Published 10:00 a.m. CT June 19, 2016 | Updated 8:32 p.m. CT June 19, 2016
"Dispatches from Pluto" by Richard Grant(Photo: Book jacket)
1. "Dispatches From Pluto,"Richard Grant, Simon & Schuster
2. "Mockingbird," Charles Shields, Henry Holt
3. "Julia Reed's South,"Julia Reed, Rizzoli
4. "Me before You," Jojo Moyes, Penguin
5. "Everybody's Fool,"Richard Russo, Knopf
6. "Rogue Lawyer," John Grisham, Doubleday
7. "Confessions of an Undercover Agent,"Charlie Spillers, University Press of Mississippi
8. "The After Party," Anton Disclafani, Riverhead
9. "Screen Doors and Sweet Tea," Martha Foose, Clarkson Potter
10. "Color New York," Emma Kelly, Harper Design
1. "Theodore Boone: The Scandal," John Grisham, Dutton
2. "The BFG," Roald Dahl, Puffin
3. "There Is a Tribe of Kids," Lane Smith, Macmillan
4. "Bug Zoo," Lisa Wheeler and Andy Harkness, Disney Hyperion
5. "Fish In A Tree," Lynda Mullaly Hunt, Nancy Paulsen Books
— Compiled by University Press of Mississippi for The Clarion-Ledger
Reported by Bay Window Books (Brandon), Main Street Books and Café (Starkville), Lemuria Books (Jackson), Lorelei Books (Vicksburg), Square Books (Oxford), Turnrow Books (Greenwood) Turning Pages, Books and More (Natchez)
Janet W. Ferguson signs "Leaving Oxford" and "Going Up South," 1 p.m. Saturday, Bay Window Books, 151 W. Government St., Brandon, (601) 706-4545
Read or Share this story: http://on.thec-l.com/24Z67mS
From Miss. to NYC: Dance group to perform at Carnegie Hall


Amit Shah, who is on a three day tour to Bengal, said sky is the limit for BJP and claimed that the party can think it has touched the sky once it succeeds in forming the government in Kolkata.
BJP President Amit Shah is on a three day tour to West Bengal.
Kolkata: Urging the state BJP leaders and activists to keep working towards the growth and betterment of the party, BJP President Amit Shah on Monday said the party would achieve its ultimate goal when it forms the government in West Bengal.
"Shah said the BJP has seen a lot of growth in recent years, but has not reached its saturation point yet. The ultimate goal of BJP would be to form government in states like West Bengal," BJP state General Secretary Sayantan Basu told IANS after a closed-door party leaders' meeting addressed by Mr Shah at the Indian Council for Cultural Relations auditorium in Kolkata.
Mr Shah, who is on a three day tour to Bengal, said sky is the limit for BJP and claimed that the party can think it has touched the sky once it succeeds in forming the government in Kolkata.
According to party sources close, he asked the party activists to stop complaining about problems and start working towards its growth at the booth level in Bengal.
Before the party leaders' meeting, Mr Shah paid floral tribute to Swami Vivekananda at his ancestral house in north Kolkata.
State BJP president Dilip Ghosh, national General Secretary Kailash Vijayvargiya, Union Ministers SS Alhuwalia and Babul Supriyo were present at both the programmes.
Mr Shah would interact with party workers from different districts, who have allegedly been victims of political violence unleashed by the state's ruling Trinamool Congress followed by an intellectuals meeting in the same venue on Tuesday.
He is also slated to hold a meeting with members of the Merchants Chamber of Commerce at a five star hotel in central Kolkata on the last day of his three day tour.


Max is so very excited about Advent this year.
After listening to Martin & Sylvia's Audio Advent he asked if he could be an Advent Helper.
The Advent Helper sends magical letters to children in the lead up to Christmas.
Max has so many advent ideas! First we needed to get craft supplies to write Advent letters to Ruby and Teddy!
He carefully cut out numbers and stuck them onto our red and green envelopes.
Next we made a list of things to do on our journey to Christmas.
And on Christmas Eve we will look at the Christmas lights in our neighbourhood.
But on the 1st of December, Max wanted us to write a letter to Ruby and Teddy explaining how advent would work this year.
I love that Max is so very excited about our Advent journey, but more than that, he wants to make it magical for his sister and brother.
Max has also discovered Sparkle Stories new Junkyard Tales audio advent. I wonder what new ideas he will have for next year now!
And if you'd like to have a listen, you can start your Sparkle Stories Free 10-Day Trial now!
What a gorgeous way to do advent! Clicking over to the website to find out more.


Address: 999 W. Dundee Rd., Wheeling IL 60090. Phone: 847-537-8270. Fax: 847-520-2710. Web site: www.d21.k12.il.us. Superintendent: Gary Mical. School Board: President Ellen Clark, Vice President Arlen Gould, Secretary Ron Smolin. Pamela Becker Dean, Kathy Edmonds, Bill Harrison, Phil Pritzker.
Address: 1050 Plum Grove Circle, Buffalo Grove IL 60089. Phone: 847-520-2750, fax: 847-419-3071. Principal: Jason Klein. Grades: 6-8.
Address: 51 St. Armand Lane, Wheeling, IL 60090. Phone: 847-520-2780, fax: 847-419-3077. Principal: LaVonne Knapstein. Grades: K-5.
Address: 221 S. Wolf Rd., Wheeling, IL 60090. Phone: 847-520-2790, fax: 847-419-3073. Principal: Tom Torchedlo. Grades: 6-8.
Address: 655 Golfview Terr., Buffalo Grove, IL 60089. Phone: 847-520-2760, fax: 847-520-2601. Principal: Kim Zinman. Grades: K-5.
Address: 1001 W. Dundee Rd., Wheeling, IL 60090. Phone: 847-520-2745, fax: 847-520-2842. Principal: James Parker. Grades: 6-8.
Address: 501 S. Arlington Heights Rd., Buffalo Grove, IL 60089. Phone: 847-520-2755, fax: 847-419-3078. Principal: Debra Serbin. Grades: K-5.
Address: 2800 N. Highland Ave., Arlington Heights, IL 60004. Phone: 847-670-3200, fax: 847-670-3216. Principal: Pam Lindberg. Grades: K-5.
Address: 1209 Burr Oak Drive, Arlington Heights, IL 60004. Phone: 847-670-3400, fax: 847-670-3418. Principal: Ed Searing. Grades: K-5.
Address: 310 S. Scott St., Wheeling, IL 60090. Phone: 847-520-2775, fax: 847-419-3074. Principal: John Patti. Grades: K-5.
Address: 515 Merle Lane, Wheeling, IL 60090. Phone: 847-520-2785, fax: 847-419-3080. Principal: Gwen Gage. Grades: K-5.
Address: 133 S. Wille Ave., Wheeling, IL 60090. Phone: 847-520-2795, fax: 847-520-2607. Principal: Ralph Cook. Grades: K-5.
Address: 700 N. Schoenbeck Rd., Prospect Heights, IL 60070. Phone: 847-870-3850, fax: 847-870-3896. Web site: www.d23.org. Superintendent: Gregory P. Guarrine. School board: President Beth Murphy, Vice President Judi Zimmerman, Secretary Linda Birnbaum. William Caputo, Kathy Gregg, Tom Klingner, Jim Perkins.
Address: 700 N. Schoenbeck Rd., Prospect Heights, IL 60070. Phone: 847-870-3865, fax: 847-870-8113. Principal: Betsy Muir. Grades: 3-5.
Address: 700 N. Schoenbeck Rd., Prospect Heights, IL 60070. Phone: 847-870-3868, fax: 847-870-3898. Principal: Sharon Warner. Grades: K-2.
Address: 700 N. Schoenbeck Rd., Prospect Heights, IL 60070. Phone: 847-870-3879, fax: 847-870-3881. Principal: Debra Wilson. Grades: 6-8.
Address: 1050 Ivy Hall Lane, Buffalo Grove, IL 60089. Phone: 847-459-4260, fax: 847-459-2344. Web site: www.district96.k12.il.us. Superintendent: Thomas Many. School board: President Marc Tepper, Vice President Victoria Juster, Secretary Renee Klass. Robert Gauthier, Deirdre Kosmin, Don Shannon, Jacqueline Walton.
Address: 1072 Ivy Hall Lane, Buffalo Grove, IL 60089. Phone: 847-459-0022, fax: 847-229-9650. Principal: Peter King. Grades: 1-5.
Address: Box 3100 RFD, Long Grove, IL 60047. 847-634-3243, fax: 847-821-7570. Principal: Greg Grana. Grades: 1-5.
Address: 1530 Brandywyn Lane, Buffalo Grove, IL 60089. Phone: 847-634-3144, fax: 847-821-7571. Principal: Paul Louis. Grades: 1-5.
Address 2600 N. Buffalo Grove Rd., Buffalo Grove, IL 60089. Phone: 847-821-8946, fax: 847-821-8949. Principal: Scott Thompson. Grades: 6-8.
Address: 777 Checker Drive, Buffalo Grove, IL 60089. Phone: 847-541-3660, fax: 847-821-7572. Principal: Barb Cirigliano. Grade: K.
Address: 6362 RFD Gilmer Rd., Long Grove, IL 60047. Phone: 847-353-8500, fax: 847-949-8237. Principal: Christine Jakicic. Grades: 6-8.
Address: 1231 Weiland Rd., Buffalo Grove IL 60089. Phone: 847-353-5650, fax: 847-634-5334. Web site: www.aptakisic.lake.k12.il.us. Superintendent: Douglas Parks. School board: President Jeffrey Newman, Vice President Gary Friedlander, Secretary Barbara Newmark, Treasurer Leslie Thompson. Cathleen Belmonte, Mindy Lehtman, Ellyn Ross.
Address: 1231 Weiland Rd., Buffalo Grove, IL 60089. Phone: 847-353-5500, fax: 847-634-5347. Principal: Theresa Dunkin. Grades: 7-8.
Address: 2195 Brandywyn Lane, Buffalo Grove, IL 60089 Phone: 847-955-3500, fax: 847-634-4229. Principal: Susan Mann. Grades: 5-6.
Address: 200 Horatio Blvd., Buffalo Grove, IL 60089. Phone: 847-353-5700, fax: 847-215-3259. Principal: Jane Kier. Grades: Pre-K-4.
Address: 850 Highland Grove Ave., Buffalo Grove, IL 60089. Phone: 847-955-3600, fax: 847-215-3268. Principal: Julie Brua. Grades: K-4.
Address: 1370 Riverwoods Rd., Lincolnshire, IL 60069. Phone: 847-295-4030, fax: 847-295-9196. Web site: www.district103.k12.il.us. Superintendent: Scott Guziec. School Board: President Pam Loewenstein, Vice President Peggy Gaier, Secretary Angela Berka. Peg Cucci, Joseph Jaeger, Kathy Santimauro, Liza Veeneman, David Wille.
Address: 239 Olde Half Day Rd., Lincolnshire, IL 60069. Phone: 847-634-6463, fax: 847-634-1968. Principal: Jill Mau. Grades: 3-4.
Address: 2425 Riverwoods Rd., Lincolnshire, IL 60069. Phone: 847-945-6665, fax: 847-945-6718. Principal: Christine Adler. Grades: K-2.
Address: 1370 Riverwoods Rd., Lincolnshire, IL 60069. Phone; 847-295-1560, fax: 847-295-7136. Principal: Katrina Heden; Grades: 5-6. Principal: Nate Carter; Grades 7-8.
Address: 2 Stevenson Drive, Lincolnshire, IL 60069. Phone: 847-634-4000, fax: 847-634-0239. Web site: www.district125.k12.il.us. Superintendent: Timothy Kanold. School board: President Les Raff, Vice President Diane Holder, Secretary Terry Moons. Steve Frost, Gary Gorson, Bruce Lubin, Merv Roberts.
Address: 1 Stevenson Drive, Lincolnshire, IL 60069. Phone: 847-634-4000, fax: 847-634-7309. Web site: www.district125.k12.il.us. Principal: Dan Galloway. Grades 9-12.
Address: 2121 S. Goebbert Rd., Arlington Heights, IL 60005. Phone: 847-718-7600, fax: 847-718-7609. Web site: www.dist214.k12.il.us. Superintendent: Elizabeth A. Ennis. School board: President William Dussling, Vice President Alva Kreutzer. William M. Blaine Jr., Lenore Gonzales Bragaw, Miriam Cooper, Stanley Eisenhammer, Robert Zimmanck.
Address: 1100 W. Dundee Rd., Buffalo Grove, IL 60089. Phone: 847-718-4000, fax: 847-718-4122. Web site: http://bghs.dist214.k12.il.us. Principal: Patrice Johannes. Grades 9-12.
Address: 900 S. Elmhurst Rd., Wheeling, IL 60090. Phone: 847-718-7000, fax: 847-718-7007. Web site: http://whs.dist214.k12.il.us. Principal: Dorothy Sievert. Grades: 9-12.
Address: 50 N. Buffalo Grove Rd., Buffalo Grove, IL 60089. Phone: 847-459-6270, fax: 847-537-2810. Web site: www.stmarybg.org. Principal: Gary Campione. School Board: President Guy Karm, Secretary Debbie Ludolph. David Galvan, Barry Sullivan, Doug Campbell, Laura Hodes. Grades: Pre-K through 8.


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Fintech is a term used to describe the companies operating in the financial technology sector. It relates mainly to small start-up companies which develop innovative technological solutions in such areas as online and mobile payments, big data, alternative finance and financial management. A good overview of Fintech with a B2C focus, including market size, business models, consumer views, blockchain technology and company profiles can be found in our Statista Report 2017. As of February 2015, bank spending on new technologies in North America was projected to reach 17 billion U.S. dollars in 2015 and increase to 19.9 billion in 2017.
Investment into the Fintech sector has grown in significance in recent years. On a global scale, the value of investment in Fintech companies amounted to approximately 31.4 billion U.S. dollars in 2017, down from its peak of 47 billion U.S. dollars in 2015. The annual value of global venture capital investment in Fintech companies is increasing.
The global growth of Fintech is dependent on its growth in individual markets, which differs greatly. As of August 2018, the Americas was the most popular location for Fintech startups. However, Fintech adoption rates in various financial sectors such as savings and investments and borrowing show that the U.S. consumers often lag behind consumers in other countries such as China, India and Brazil. An overview of the U.S. Fintech companies can be found in our Statista Report 2017. Surprisingly, only 14 percent of Americans have heard about Fintech companies, and have a clear idea of them.


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Postfixbuch mailing list, for the book by Peer Heinlein.


Is Xbox One getting semi-transparent tiles soon?
Could Microsoft be planning to give the Xbox One home screen a bit of a refit? Check out the above screenshot that was taken by one eagle-eyed Redditor named "mkmkd", who was watching a Twitch livestream by Microsoft's Larry "Major Nelson" Hryb and spotted the change. Notice how the tiles, rather than being solid blocks of colour are now semi-transparent. It looks nice, too.
Microsoft hasn't said anything official about this, and it is conceivable that it could decide against the change – though if it happens, it'll be an interesting update for Microsoft's design, and it'll be interesting to see how it cascades across all of Microsoft's other platforms. Could Windows Phone and Windows 10 soon start looking similar?
Windows phone already has transparent tiles.


text:
Young Leaders Bring Diverse Experiences and Vision to GTCF's Youth Philanthropy Board
Filed Under: Youth Philanthropy Board - Posted @ 3:31pm
Sometimes it can take a while for a new group to feel comfortable working together. One thing GTCF Program Director Janece Levien appreciates about the new members of this year's Youth Philanthropy Board (YPB) is their willingness to embrace discomfort in an effort to cultivate positive change.
"After a conversation at our last meeting around identity and place in community, one member said that she was uncomfortable, but that it was a good uncomfortable," Levien recalls. "With this type of mindset, I know these youth have potential to strengthen identity, voice, and a true sense of equity for communities in Pierce County."
The 15 new members of this year's Youth Philanthropy Board are a diverse group of emergent leaders from schools and service learning programs across Pierce County. Ranging in age from 15-24, they bring a spectrum of skills and experiences as well as a shared dedication to giving back to the community that gave them opportunities to succeed.
"I have experienced many hardships being a low income woman of color from Tacoma, so I know the needs of people from Tacoma first hand. That is why I have developed a deep care in furthering opportunities for others to be successful despite what barriers they may be facing. I am a young activist and believe in utilizing my voice to support others." – Zayda, age 18
"Youth can play a substantial role in making our community better. Today's youth are the leaders of tomorrow and as we find ways to be part of the community and are given the resources to explore our passions and interests, we can grow as a group and create new communities, thereby strengthening the whole." – Rachel, age 17
"I aim to educate, empower, and engage youth in my community so that they realize the power and potential they have within themselves to become masters of their own future in the City of Destiny." – Dylan
Over the course of the next nine months, these YPB members will have the opportunity to participate in workshops focused on professional development to promote and elevate youth voice in Pierce County. In addition, they will play a role in providing resources to local youth-serving organizations.
"The goal of YPB is to bring youth voice into community decisions", says Levien. "When youth are given an opportunity to discuss topics that directly impact their daily lives, there is an investment in critical thinking and essential life skills that go beyond the work of this cohort to impact our community in a positive way."
2018-19 GTCF Youth Philanthropy Board Members
Annalycia Matthews, University of Washington – Tacoma
Audrey Medisch, Wilson High School – Running Start
Brooklyn Hose, Curtis High School
Cristobal Luevanos-Perez, Mt. Tahoma High School
Dylan Tran, University of Washington
Emily Schell, AmeriCorps – Tacoma Rescue Mission
Harold Liufau, University of Puget Sound
Lucy Gould, Stadium High School
Musa Abdirahman, University of Puget Sound
Ngocbich Tran, Science & Math Institute
Noe Rosales, Mt. Tacoma High School
Rachel Nunes, Curtis High School
Rita Tumbusch, Science & Math Institute
Tori Brockman, Curtis High School
Zayda Wilson, AmeriCorps – Peace Community Center


text:
Woog's World / Breaking free of the corporate world
Dan Woog
March 14, 2019 Updated: March 14, 2019 4:28 p.m.
Two hundred years after its launch, HMS Beagle is still the British Navy's most famous sloop. Charles Darwin was on board for its second voyage. His research changed the world.
Today, the HMS Beagle is the name of a company. It comes from Darwin's famous quote: "It is not the strongest of the species that survives, nor the most intelligent, but the most adaptable to change."
HMSB is a strategic consulting firm. Its mission is to help clients navigate the journey to survival. They "equip the next generation of brands — from start-ups to legacy companies — to weather the perfect storm of brand extinction, talent erosion and business model implosion."
Joseph Jaffe founded the company. It's his third startup, following the equally intriguingly named Evol8tion and crayon.
His title at HMSB is "Admiral." That says all you need to know about the creativity and outside-the-box thinking of the longtime Westporter.
Like so many others, the Admiral — er, Jaffe — took a circuitous route here. The South Africa native fell in love with a fellow Cape Town resident named Terri. In 1993 she got a job with Sotheby's in New York City. He stayed home, helping Nando's Peri Peri Chicken roar to success with an irreverent marketing campaign (they used sex, religion and politics — the three taboos — to great effect).
After four years apart, Jaffe moved to New York. He followed love and, like many immigrants, saw a substantial future for himself in the States.
He worked in advertising, then in 2002 ventured out on his own. He's been a consultant and entrepreneur ever since. His corporate experience inludes Nestle, Purina, Colgate-Palmolive, Keurig and Dr Pepper.
Along the way, Jaffe wrote four books. His fifth — "Built to Suck: The Inevitable Demise of the Corporation ... and How to Save It?" — was published this month.
Its thesis is that the very thing responsible for the success of corporations — size, scope, scale — is now the single biggest contributor to its demise. Large companies have lost their competitive edge. As the world speeds up, they're slowing down.
"The corporate legacy world of lethargic, conservative, risk-averse, dysfunctional, siloed, political shenanigans is coming to a close," Jaffe writes.
Bob Liodice put it in even more dire terms, after reading an advance copy. The CEO of the Association of National Advertisers said, "Joseph Jaffe has done it again. This time, he's brilliantly demonstrated how companies' ages are just like people. Corporations must stay young, fit and agile — or face the outcomes of company weight gains, vision problems, hearing difficulties, flab, dementia and an early death."
There is, thankfully, hope. Survival, Jaffe says, is possible, with a "four-pillar" antidote. But companies must "embrace their inner heresy. It is only through this cathartic process that transformation can occur."
It's a scary thought, for the millions of men and women working in dinosaur-like corporate America. As for Jaffe, he works largely in Westport in a very 2019-ish, totally un-corporate setting. He is a poster child for the anti-company.
Jaffe does much of his writing and consulting at B:Hive Southport. That's the new co-working space on the Westport border. It features everything a modern worker needs — desks, conference space, private rooms, fast internet, plenty of power outlets, a kitchen, the chance to meet and collaborate with like-minded individuals — all with killer views of the Sasco Creek waterfall.
At B:Hive, Jaffe is surrounded by men and women doing intriguing things. "The next Industrial Revolution is entrepreneurship," he notes. "It's a huge cultural change. People can now be productive, and work together, regardless of where they live or who they are."
He has "tremendous respect for people who do the Metro-North commute every day." But when they get to the city, he says, they work for companies that are "increasingly not friendly to people. He cites layoffs and a lack of loyalty, along with all those other forces mentioned earlier that he believes will kill corporate America.
B:Hive — and the rest of Westport, with its critical mass of bright, creative, energetic people — is "a great place for people with different skill sets" to find each other, collaborate, and help build whatever will replace big, lumbering (and dying) corporations.
Meanwhile, Jaffe's life here is far from all work and no play. He says, "Cape Town has been called the most beautiful city in the world. But Westport must be the most beautiful town. I absolutely love it."
He is very involved in the Westport Soccer Association, as a coach for his three boys' rec and travel teams.
He is also the cantor at Chabad of Westport. Jaffe leads Friday and high holiday services, and he sings with the School of Rock in Fairfield.
Dan Woog is a Westport writer, and his "Woog's World" appears each Friday. He can be reached at dwoog@optonline.net. His personal blog is danwoog06880.com.


4Wheelz.net Get 1966 oldsmobile & latest cars with American Auto Legends.
O ur history starts back in 2003 when a few car enthusiasts decided to gather together the most significant American cars of all time-we call them "Legends". Our goal was to provide this information available on-line and for free so that everybody who has interest in American cars can get it. Before long we decided to give our visitors ability to show off the cars they own. We gave this part of our site name of "Virtual Parking". Now days we have the largest "Virtual Parking" yet with great many cars from various makes and countries.


Side effects and experiences with Synthroid by users like you. Post Yours Now.
I also gained weight, work out 4-5 days a week and havent lost a pound. I am now having a vibrating or shaking feeling through out my body that is worse at night. I tried to stop taking it but that was worse.
I too have experienced granuloma on the top of my hands ande many unsightly rings and blotches on my legs from the knees to the thighs. Help! Have you tried another medication for hypothyyroidism in synthroid's place4?


For years I've wanted to learn to meditate but never taken the time to find out how to do it. I've read books and most say you have to clear your mind of all thoughts! Well, for me that's almost an impossibility! My mind goes a million miles an hour, with thoughts jumping in and out constantly! How on earth would I clear them away or at least deflect them?
It appears that the "traditional" forms of meditation, like Transcendental Meditation, are not the only ways. Teresa, my Body Talk buddy and long-time friend, told me that she does this meditation, or Ascension, every day, and that you can do it with your eyes open, too, during the day and it helps deflect stress.
When I looked at the web site, one of the things that attracted me was that it works on stress, the root stresses of life. And as stress has been a major factor in my life, and I believe, a huge part of why I got sick, I decided to take the First Sphere weekend.
Now, I am a skeptic. I wanted to learn the techniques and get the tools but hadn't really thought about it much further than that. For once, I hadn't researched the new endeavor and went into it fairly blind!
Although I'm a Science of Mind person, believing in the One Universe, not a figurehead god, my beliefs have always been sort of behind the scenes, and not for public consumption! But when you go to something like this weekend, thoughts and things are put to the test! And why pay the money if you aren't going to participate fully?
So, I did - well, to the best of my ability.
Susan was the only other newbie, and Teresa and David were there (seeing at they were sponsoring the weekend and it was held at their house - our old house) and other more advanced Ascenders as "we" are called, came and went during the weekend. Our teachers, Parjanya and Vidya are called Red Ishayas ( there are red, white and black Ishayas) who work from the space of Unconditional Love.
Friday evening was the first part, and I went early as Teresa was to do a Body Talk on me - I had missed the mid-week session because the weather was so crummy and I didn't want to drive at night on icy roads. When I got there, David was in the kitchen cooking up a storm! A long time ago, Teresa came home one evening and said, "I have no more recipes to cook, you can do it now," and since then, David has done the cooking, and he is good! We had a good spaghetti dinner and I had my BT session and then Susan arrived and we started into the weekend.
We were instructed on the basic concept behind the Ishaya tradition and began our first Ascension. From then on, throughout the entire weekend, it just got better and better, and I learned how to deflect thoughts that creep in, and doing it without effort. It was an amazing experience and the skeptic in me was shamed into submission!
Of course, there is the side of the it that affirms that the more people practise this teaching, the more peaceful and in harmony the world will become. And I do believe that, yet this is where my skeptic is currently living! But I believe that if I practise this teaching ( at least once a day for 20 minutes - but recommended 20 minutes three times a day) I will be putting out good vibes into the Universe that can only help.
But the main thing for me is relieving the stress, which I know it does. It was a physical feeling when I Ascended - a sinking, or dropping off, the tightness in my chest just fell away and that's where I carry stress. It also gave me the tools to deflect darkness and negativity during normal activities, doing it with my eyes open. Now this is going to take some practise, but they say if you just say the words, it works. So when I run into road rage, out come the words and away go the nasties! Ha! We'll see, or maybe I will just be able to kiss off their rage and go my own way without getting caught up in it. Or something like that!
Now my normal morning routine will include a meditation. It works out well with Zoze's daily remote Reiki so I sit in the recliner, usually with Hinckley in my lap, and get totally zenned out! Most relaxing and very good for me. I am feeling so much better these days - whether it's just time healing me, or the holistic measures like the reiki, body talk and meditation, supplements and slower pace, or the prednisone doing its thing, I don't know. And I don't really care as long as the recovery continues and I keep getting better and better.
It doesn't hurt that the weather has been better the last two days, with sun and brightness! Still cold, but the plum tree is blooming, the pink plum trees are in flower all over the county and I even have a few little hyacinths popping up. So spring is sort of here and so there is hope for a summer!


People across Norfolk are being encouraged to help spread the word that our county is a place where everyone can feel safe and proud to be themselves for Norfolk Day 2019.
This year, Norwich Pride will take place on Saturday July 27, the same date as Norfolk Day.
A celebration of the LGBT+ community for everyone, this year Norwich Pride will mark the 50th anniversary of the Stonewall riots which took place in New York in June 1969.
Already a day focusing on celebrating what a wonderful place our county is to live for everyone, the organisers of Norwich Pride said they were excited to be sharing their day of celebration with Norfolk Day.
Michelle Savage, chairperson of Norwich Pride 2019, said: "Every year thousands of people come into the city from across Norfolk to celebrate Norwich Pride.
"We are delighted to be working with Norfolk Day to make Saturday July 27, 2019 the happiest and proudest day in the county's calendar.
Last year thousands of people came to Norwich to celebrate Pride, with organisers heralding it the most successful event to date. Further details of events taking place for this year's Pride will be announced nearer the time.
Details of all events and those taking part in Norfolk Day will be revealed in this newspaper, online and via social media in the run up to the day.