text:
Addressing affordability to improve access to healthcare
The value of healthcare goes beyond the patient
We believe healthcare spending is an investment. Accurately diagnosing and treating the right patient at the right time with an innovative medicine can bring tangible benefits beyond the individual and have a positive impact on society as a whole. The value of innovations has many components such as disease prevention through screening or early diagnosis, as well as enabling people to go back to work and care for their families thanks to targeted treatment solutions and improved compliance. That's why we continue to drive innovation, so that our tests and medicines can make an even bigger difference to more people.
Causes of lack of healthcare affordability
Each country assesses the value of healthcare in different ways and has different priorities based on the local situation. Relevant factors include its overall wealth (and health), the stability of its economy, the distribution of funds to healthcare, the existence of a national health system and how it is set up, including any local Health Technology Assessment organisations, the level of personal health insurance in the country and the average income and available means of its citizens. There are many countries where funding levels for several or all of these elements are inadequate.
What are we doing
For over 10 years, we have worked with public and private partners to identify and solve gaps in healthcare financing. Our aim is to find tailored solutions that are sustainable for patients, governments and payers as well as for our business by enabling us to continue to invest in vital innovation.
Roche has supported the creation of innovative financing mechanisms and dissemination of knowledge to contribute with practical solutions to the real problem faced in many countries: insufficient funding for high-cost therapies. By partnering with traditional and non-traditional stakeholders such as governments, banks, telecommunications, insurance and data start-ups we are creating funding solutions in countries where public coverage may still be inadequate to help improve patients' financial protection and increase health system sustainability.
We are responding to the need for tailored pricing approaches by offering a number of pricing models that are designed to address payers' needs and concerns around affordability, benefit and impact, such as performance and finance-based agreements. Our pricing strategy reflects the World Health Organization's definition of "Fair Pricing", balancing the need for both affordability and incentives for innovation.
One of the ways that we believe we can help address affordability and access challenges in low and lower-middle income countries is through our newly strengthened International Differential Pricing (IDP) approach. This differential pricing approach allows our local organisations to adjust prices to reflect a country's relative income and their ability to pay, ensuring that our innovations are fairly priced and therefore reach patients in need.
The WHO has described health as a human right, requiring access to timely, acceptable and affordable healthcare and providing for the underlying determinants of health, including safe drinking water, sanitation, food, housing and health education.
To make access to healthcare affordable and protect patients from financial burden, both the private and public sector share the responsibility to work together to build resilient and sustainable healthcare systems. To support our stakeholders, we are building partnerships for tailored solutions that are key to improving access, particularly affordability. We are developing new approaches to how we price our medicines and we are working with governments that are keen to invest in strong and resilient healthcare systems. As a result, we are seeing improved access around the world: almost 4 million people were treated with Roche's core and established products in low and lower- and middle- income countries between 2017 and 2021 In 2021, we also implemented pandemic pricing strategies for our COVID-19 medicines and expanded our Diagnostic Global Access Programme to include SARS-CoV-2 PCR testing for improved access in low and lower middle income countries.
See more access stories


Swindon News | December 4, 2017 by Richard Wintle | Comments Off on Father Christmas' attend the fun run.
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Two Hundred Santa Claus hit Lydiard Park for a fun run organised by Swindon Phoenix Rotary.
The run raised funds for the Air Ambulance which put in a flying visit.
« Olympic star turns on Old Town lights.


text:
How Netflix is dragging Hollywood into a global, one-market future
Two top Netflix execs talk about wrangling with Hollywood, how they measure success and the future of the platform
Jan 26, 2016 Peter Nowak
Netflix Chief Product Officer Neil Hunt. (Jasper Jacobs/AFP/Getty)
There's no doubt that Netflix was among the biggest newsmakers at the Consumer Electronics Show in Las Vegas a few weeks ago. The streaming company made a big splash with an announcement that it was expanding to a further 130 countries, bringing its grand total to nearly 200.
Netflix CEO Reed Hastings on the power of doing one thing well
Along with a few other journalists, I had the chance to sit down at CES for a chat with Neil Hunt, Netflix's chief product officer, and vice-president of content acquisition Elizabeth Bradley, about what the expansion means for the future of Netflix and streaming video in general.
We talked about a broad range of topics, from how the company differentiates itself across its territories to the 4K and high-dynamic range (HDR) technology it's pushing, the vagaries of country-level licensing for a global entertainment business, and how to measure success outside of the traditional TV ratings game.
Why aren't you launching in China yet?
Hunt: China is a much more complicated market because we have to get licenses and permission from the Chinese government, we have to work with a local partner.
We chose to separate that from launching in 130 countries because that's tricky enough. We do anticipate launching in China, hopefully soon, within the next few months.
How different would Netflix look there compared to countries that don't have those restrictions?
Hunt: We hope to launch something that looks very similar, but we're not at that point yet.
Is adding 130 countries going to increase your power in dealing with studios for global rights to content?
Bradley: We obviously have put a large emphasis on making Netflix original series where we are the commissioner globally, so we can offer the same package on the same day around the world.
When you license a show that the studios have already put into multiple windows around the world, it's challenging to try and sync those up on the same day. But the studios are our partners in these shows… we're making shows with all of them. So it doesn't necessarily change [that relationship].
The studios are moving along with us in how they distribute. I don't know if I'd phrase it that we're stronger.
The element we're really excited about is that if you think about Bollywood content, it mostly resonated in India. There really are no platforms in Sweden [for example] that offer it, and that's something we can do. The anime market is big in Japan and we can help bolster that content around the world.
Hunt: That's putting the power in the hands of the producers because they have a much wider audience to reach. There's growth and development for everyone.
Do you have to get local content in each country to make it attractive for subscribers there?
Bradley: Great storytellers tend to resonate across markets so it wasn't as if we went to Korea and licensed content only for Korea and nowhere else.
It's pretty consistent in the type of offering we're going after. There's differences in what's available, but we're pretty conscious of having shows that resonate everywhere.
You can buy something like Breaking Bad and be sure that's going to work in every territory.
Why 2016 is going to be a breakthrough year for 4K television
Are you going to prioritize 4K over HDR, or vice versa?
Hunt: The strategy is to provide 4K with and without HDR and to provide HDR at a variety of different resolutions, starting well below 4K.
HDR requires about 20% more bandwidth than the equivalent resolution of non-HDR. Somebody who has a six-megabit DSL connection, for example, is probably able to get 720p and maybe full HD [1080p] HDR, but they won't get 4K with or without HDR.
Do you think customers care about HDR?
Hunt: We've had many increments in pixel resolution over the years and that's great, but the marginal returns in moving up to 4K aren't as great as the earlier steps. Equivalently, we have not had improvements in dynamic range or colour space at nearly the [same] pace and scope.
This is really the first step toward improving the picture in pixel depth and colour space. I think it's going to be more important and more relevant to more people than moving to a higher resolution.
In the past you've said it could take five years for 4K to go mainstream. Is that still your timeframe or do you see it accelerating?
Hunt: I think I said last year that three to five years until more than half the devices sold will be 4K. I think I stand by that.
I think I also said three to five years until most content is produced with 4K in mind. I'd stand by that too, although it could be accelerating. It's in that magnitude.
I think you can count on a similar three-to-five-year growth period for HDR devices.
Are you seeing big growth in the number of customers signing up to your 4K service?
Hunt: Yes we are, that's a gratifying outcome. We have well over two million households that engage with Netflix with a 4K device and an increasing number of those who are buying the 4K package, although I won't say what number. It's definitely a growth area for us.
Can you clarify CEO Reed Hastings' comments about the Adam Sandler movie, The Ridiculous 6, being Netflix's most-viewed film?
Bradley: It's been out for about 30 days, so relative to other titles in their first 30 days, it's our top-performing feature.
We obviously made a big bet with making four pictures with Adam Sandler because we feel that he will resonate.
So do Netflix's star ratings have any bearing on their popularity? That movie has one star.
Hunt: The star ratings are driven by our members based on how much they enjoy the content. The version that you see is based on people who have similar tastes to you and how they'd rate it. It's not an average, it's an average of people like you.
The art and science of how Netflix chooses its original series
If it's one star, then you and I are close together because we're not Adam Sandler fans, which is fine.
The problem with stars is that people tend to use them to represent quality rather than enjoyment. Sandler is a classic case of something that can be perceived as low-brow, but can actually be quite fun to watch. How do you put stars on that?
We're experimenting with a like/dislike signal and a per-cent match, so this is an 86-per-cent match for your tastes and interests. It may better capture the enjoyment factor.
How much are your decisions to license such content based on data versus the traditional way that networks acquire content?
Bradley: It's way more weighted toward the first one, we don't necessarily think of the way that traditional TV networks go about it.
We look at the data and that's why we made the bet on Adam Sandler. Every film we've had in different countries continues to resonate. Critics don't ever seem to like his films but that's not what's driving it.
How much better has your understanding of viewer data become since House of Cards?
Bradley: What our original series have been very conscious of is what can we license in the market based on what's traditionally been available on linear networks and what are they not making?
Orange is the New Black is very different than what's been on linear, Sense8 is something that would have had a hard time on linear, Grace and Frankie for people over 65.
We've been very conscious of trying to keep the diversity and that's what we've learned from the data.
How do measure success of your shows and movies?
Bradley: We look at the viewing, on a couple different levels. How quickly do you watch it, how long do you watch, if you have a series do you watch all 10 or do you leave after two?
We have the ability to take a piece of content and better direct it to an audience that's going to like it. We don't have to have huge box office hit type films or big shows that get the best ratings on a Sunday night because we have the ability to target to the right audience for everything. We can better monetize smaller shows.
Hunt: We measure hours, but that's not it because that's way too crude. We measure valued hours.
Somebody who doesn't watch very much presumably puts more value on the hours that they do watch than someone who's watching a hundred hours a month.
Print books are successfully fighting off digital competitors
We're able to measure the contribution of valued hours in a way that matches well to the business value of how it attracts your attention and the economic value of that customer.
There's a lot of sophistication that we've developed over the last few years to develop that data and interpret it into metrics that inform the buying.
It seems like you're pushing your own original content more than your licensed content?
Hunt: We have a problem to solve with originals. With licensed content we can leverage the awareness that's been generated from someone else's marketing campaign. With an original we have to build that awareness from scratch. It requires a bigger presentation, maybe more frequent repetition to get over that hurdle.
The primary metric driving all of our choices and presentation is customer attention. If we overweight our originals and you end up choosing something that's less good for you than licensed content, you may or may not churn off. Somebody like you will churn off and it'll be reflected in the metrics and we'll realize we've overstepped.
Conversely, if we don't push originals hard enough and you end up choosing licensed content instead when an original might have been a better choice, not only might we potentially damage retention, we won't get the advantage of an original as something that only Netflix has.
There's tradeoffs on both sides and we try to balance it. We've found we definitely can go too far, but we also have to push quite hard to overcome the lack of awareness, so that may be why you see things frequently.
How is it that Canada is the only country that's getting Star Wars: The Force Awakens in 2016?
Bradley: In the feature film business, there's currently a windowing structure that exists from the studios. We have what's called the "Pay 1" window, which is seven to eight months after the theatrical run. We have that deal with Disney in Canada [for December], and we're picking up that deal in the U.S. in January.
It's a real challenge. Movie licensing is not global and we're a global company. The way you get around that is you make your own movies, which is what we're doing to help motivate that [change].
So to go back to the earlier question: will this pressure the studios to license movies globally?
Bradley: [Head of content acquisition Ted Sarandos] said it beautifully: we're not anti-theatre, we're pro-movies. We're just trying to give that to consumers. Everyone is just listening to what customers want. There's no reason customers in Thailand should wait longer than customers in Ireland.
How the digital age turns competitors into collaborators
Hunt: If you have a property and you space it out over time in countries, the consumer behaviour is that they want it anyway and they're going to find it somehow.
If you release it all over all at once in a cheap, easy-to-access, quality manner, we demonstrate that you can reduce piracy in general.
The data from Australia is that we reduced BitTorrent traffic by 17% over the first few months after launching. You reduce that opportunity.
So is the end goal to be able to access the same content from anywhere in the world?
Hunt: That certainly is a huge ambition. It may take 10 or 20 years or forever to happen, but we'd love to get there.
If you have the same content in each country, doesn't that risk Netflix being homogenous?
Hunt: On the contrary. If you think of any one channel or network or broadcaster in any particular country, it's very homogenous. It doesn't address a broad audience. It addresses a subset of that audience.
Look at HBO and you can tell what theme it has. It's gritty and it's a mid-age audience. It's not kids and it's not 50-year-olds. That's great, but Netflix is a platform that can deliver that content, and kids content and Grace and Frankie and Bloodline to the slightly older audience.
By using the recommendations, we can present one set of content to you and another set to your parents and another set to your kids, so we can address all those markets.
Do you have any idea on what the price ceiling is for Netflix yet?
Hunt: Median consumers in the countries that we're in right now consume 30-ish, sometimes 40 hours a month. That's a big number.
If you take $8 and divide it by 30 hours, it's a few cents an hour. Compare that with renting a DVD from the local rental store or almost any other form of entertainment and you would conclude that Netflix is significantly cheaper than most.
In terms of a price ceiling, I don't think we're near it. On the other hand, we get tremendous traction for being a very good value to consumers so I don't think you should expect to see it race upwards.
Are you doing any experiments with live event streaming that you can tell us about?
Hunt: We are not doing experiments with live. We think one of the values of Internet television is that it's at the time you want to watch it, not at the time it was broadcast. Traditional broadcast does a rather good job with live so we'd be playing somebody else's game and not the game we play well.
The move we're about to make is the Chelsea Handler talk show and the Beastmaster game show*, both of those will be not live, but not 10-year properties either. That gets to a shorter lifetime and more frequently updated. We'll see how that goes. [*This show has not yet been officially announced by Netflix.]
But it's unlikely we'd want to get into live in the short term.
Lilly Singh, IISuperwomanII and the future of YouTube
Have you thought about adding social components that let viewers watch together at the same time?
Hunt: Yes, we've played with social components for a long time and we've had three major attempts at it. None of them have worked well so we've retired them all.
The social piece we have is the generic sharing panel that lets you post to Facebook and Tweet and so on. There's too much effort for people to invest in building a new and differentiated social network to share their show recommendations and not just piggy back on the tools they've got.
There are also too many concerns about privacy. Some sort of automatic linkage is just toxic. We've experimented and explored and it doesn't work. If you want to share, you hit the Facebook button and it shares to the people you want to share with.
Do you think Steven Avery is innocent?
Hunt: I don't presume to know. I haven't watched [Making a Murderer] yet.
You're the only one in the world.
Hunt: Not everything appeals to everyone. I watched a few minutes and decided to go back to Jessica Jones.
MORE ABOUT NETFLIX, STREAMING VIDEO & DIGITAL MEDIA:
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Why 2016 is shaping up to be the year of digital burnout
How marketers are responding to the rise of ad-blocking
The art and science of how Netflix greenlights its original series
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Change Agents 2016: Hamed Shahbazi, TIO Networks
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redpajama_set_name: RedPajamaCommonCrawl


text:
Canopy Growth Has Begun Lobbying In The US. What Is The Company Up To In New York?
Canopy Growth, a Canadian cannabis company and one of the world's largest, is expanding its lobbying efforts in the United States. Check out awesome wedding cake strain in our space monkey meds category
Cannabis Wire first reported the company's US lobbying efforts last week, noting that California-based firm Platinum Advisors registered on behalf of the company. In Congress, Canopy Growth is focused on the SAFE Banking Act, the STATES Act, and "cannabis and hemp agricultural issues," according to lobbying disclosures for the spring. But its efforts aren't limited to federal legislation.
Cannabis Wire also has donkey butter strain and has found that Canopy Growth has also been lobbying in New York state since late March. The company is in the process of setting up its multi-million dollar, 308,000 square-foot "hemp industrial park" in upstate New York, the location for which was secured in April. The company is also in the process of acquiring Acreage Holdings, which holds one of ten medical cannabis licenses in the state.
(Read Cannabis Wire's story on Canopy's upstate New York industrial park here).
Cozen O'Connor Public Strategies, a New York-based lobbying firm that also lists Atlanta-based medical cannabis company Surterra Holdings as a client, registered on behalf of Canopy Growth. Eight lobbyists from the company have been engaging legislators and government bodies around "cannabis/marijuana" and "legalization" in the state.
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text:
\section{Conclusion}
  We present \mbox{\sc WarpGate}\xspace, a system prototype for semantic join discovery in Sigma Workbooks. In future work, we expect to share our experience and lessons of deploying \mbox{\sc WarpGate}\xspace in the production environment and conduct a user study to further evaluate its value for end users.
\section{Experiments}
  We evaluate \mbox{\sc WarpGate}\xspace on effectiveness and efficiency in comparison with two existing data discovery systems that support enterprise use cases. We obtain promising results of our embedding-based approach and show that it is sample-efficient and scales well to tables of millions of rows.

\subsection{Datasets}
  We use three repositories for evaluation. Table~\ref{tab:datasets_stats} gives a summary of dataset characteristics.

\begin{table}[h!]
\caption{Basic statistics of evaluation datasets. XS is shorthand for testbedXS in the NextiaJD repository, similarly for S, M, and L.}
\label{tab:datasets_stats}
\resizebox{0.95\columnwidth}{!}{%
\begin{tabular}{cccccc}
\hline
\multicolumn{1}{l}{}     & \multicolumn{1}{l}{\textbf{\# Tables}} & \multicolumn{1}{l}{\textbf{\# Columns}} & \multicolumn{1}{l}{\textbf{Avg. \# Rows}} & \multicolumn{1}{l}{\textbf{\# Queries}} & \multicolumn{1}{l}{\textbf{Avg. \# Answers}} \\ \hline
\textit{\textbf{XS}}     & 28                                     & 257                                     & 1,938                                     & 35                                      & 2.8                                          \\ \hline
\textit{\textbf{S}}      & 46                                     & 2,553                                   & 209,646                                   & 177                                     & 3.6                                          \\ \hline
\textit{\textbf{M}}      & 46                                     & 1,067                                   & 3,175,904                                 & 188                                     & 4.4                                          \\ \hline
\textit{\textbf{L}}      & 19                                     & 541                                     & 12,288,165                                & 92                                      & 3.6                                          \\ \hline
\textit{\textbf{Spider}} & 70                                     & 429                                     & 7632                                      & 60                                      & 1.1                                          \\ \hline
\textit{\textbf{Sigma}}  & 98                                     & 1,343                                   & 2,243,932                                 & TBD                                     & N/A                                          \\ \hline
\end{tabular}%
}
\end{table}

  \subhead{Nextia\textsubscript{JD}\xspace.} Flores et al.~\cite{DBLP:conf/edbt/0002N021} composes four testbeds of datasets from open repositories such as Kaggle and OpenML. Datasets are divided into testbeds according to their file size. For instance, Nextia\textsubscript{JD}\xspace-XS contains datasets of size smaller than 1 MB while Nextia\textsubscript{JD}\xspace-L consists of datasets of size larger than 1 GB. They also label the join quality of pairs of attributes based on a measure that considers both containment and cardinality proportion with empirically determined thresholds. In experiments, we use attribute pairs with quality labeled as Good and High by ~\cite{DBLP:conf/edbt/0002N021}.

  \subhead{Spider.} Released as a large-scale semantic parsing and text-to-SQL dataset, Spider~\cite{DBLP:conf/emnlp/YuZYYWLMLYRZR18} includes 5,693 SQL queries on 200 databases across domains. We parse schema SQL files and retrieve join paths between primary keys and foreign keys as ground truth. Spider has both the training set and the development set, and we currently use join paths from the development set for evaluation.

  \subhead{Sigma.} The Sigma Sample Database is a collection of schema and tables that are accessible to all Sigma Computing accounts for exploration.  It is stored in a Snowflake account managed by Sigma Computing and changes over time.  The datasets in this database vary in origin; some are real, publicly accessible data, some are real data that has been obfuscated, and some are completely auto-generated data. The domain of the data ranges includes retail (transactions, products and stores), financial (daily attributes of many securities), demographic (census, restaurants and bikes) and usage (cloud application usage and metering, server logs).

\subsection{Baselines \& Metrics}
  For comparison, we consider as baselines two system prototypes  Aurum~\cite{DBLP:conf/icde/FernandezAKYMS18} and {D$^{3}$L}~\cite{DBLP:conf/icde/BogatuFP020} that report on real-world data discovery (see Section~\ref{sec:related_work} for more information). Note that Aurum stores detected relationships in a graph data structure and does not support top-$k$ search.

  When ground truth is available, we report top-$k$ precision and recall with varying $k$. At each value of $k$, we average the precision and recall numbers over all the queries. We also limit $k$ to a small range since the average number of answers to a query is small in evaluation datasets, and we don't want to overwhelm users with too many recommendations.

  Besides effectiveness, we consider index lookup time and end-to-end query response time for efficiency measurement. Index lookup time refers to the time for the index data structure to return top-k candidates, whereas end-to-end query response time is the total amount of time for a discovery system to respond to a query, which includes data loading and embedding inference time in our case. We report both metrics in seconds averaged over all queries in a dataset. It is crucial in our application scenario that the system responds to queries at interactive speed.

\subsection{Experiment Results}
   Based on considerations in Section~\ref{subsec:warpgate_prototype}, we choose to use Web Table Embeddings~\cite{DBLP:conf/sigmod/0002TGL21} as our underlying embedding model. We set the similarity threshold of the SimHash LSH index to 0.7 and run baselines with their default setting. All experiments are conducted on an Amazon EC2 p3.8xlarge instance.

\subsubsection{Join Discovery on nextiaJD Testbeds}
  \begin{figure*}[!ht]
    \centering
    \minipage{0.33\linewidth}
      \includegraphics[width=\linewidth]{figures/quality_nextiaJD_testbedS.pdf}
      \vspace{-0.8cm}
      \caption*{(a) testbedS}
    \endminipage\hfill
    \minipage{0.33\linewidth}
      \includegraphics[width=\textwidth]{figures/quality_nextiaJD_testbedM.pdf}
      \vspace{-0.8cm}
      \caption*{(b) testbedM}
    \endminipage\hfill
    \minipage{0.33\linewidth}
      \includegraphics[width=\textwidth]{figures/quality_spider.pdf}
      \vspace{-0.8cm}
      \caption*{(c) Spider}
    \endminipage\hfill
    \caption{Top-$k$ precision and recall.\label{fig:quality_nextiaJD_spider}}
    \vspace{-1em}
  \end{figure*}

  We present discovery results on Nextia\textsubscript{JD}\xspace testbedS and testbedM in Figure~\ref{fig:quality_nextiaJD_spider}(a) and ~\ref{fig:quality_nextiaJD_spider}(b) respectively (results on four testbeds have similar trends while these two testbeds contain more tables and queries than the other two). As shown, \mbox{\sc WarpGate}\xspace consistently obtains higher precision and recall compared with two baselines as $k$ increases.

  \begin{small}
  \begin{table}[h!]
    \centering
    \caption{End-to-end query response time in seconds/query (for $k=10$) along with index lookup time reported in paratheses for \mbox{\sc WarpGate}\xspace.}
    \label{tab:query_response_time}
    \resizebox{0.7\columnwidth}{!}{%
    \begin{tabular}{cccc}
    \hline
                               & \textbf{Aurum} & \textbf{D$^{3}$L} & \mbox{\sc WarpGate}\xspace \\ \hline
    \textit{\textbf{testbedS}} & 0.18           & 4.77         & 3.12 (1.04)             \\ \hline
    \textit{\textbf{testbedM}} & 0.03           & 57.69        & 38.73 (8.39)            \\ \hline
    \end{tabular}%
    }
  \end{table}
  \end{small}

  Table~\ref{tab:query_response_time} shows end-to-end query response time. Aurum is significantly faster because it indexes detected relationships in another graph data structure for result retrieval. In contrast, D$^{3}L$ is the slowest since it uses an ensemble approach and aggregating multiple types of signals takes longer. As we can also see here, given the equal number of tables in two testbeds, when the average number of rows increases by an order of magnitude, query response time increases linearly and becomes unacceptable for interactive purposes. Moreover, index lookup time accounts for less than 25\% of query response time on testbedS and less than 13\% on testbedM. This suggests that there are other bottlenecks in the pipeline and improving only index lookup time (as many earlier works focus on) is not enough to reduce overall query response time. Upon inspection, we found loading large datasets from disk to memory and embedding inference take most of the time besides index lookup. We show in Section~\ref{subsec:sample_efficiency} that sampling is an effective mitigation and our embedding approach is robust to sample size.

\subsubsection{PK/FK Detection on Spider}
  Figure~\ref{fig:quality_nextiaJD_spider}(c) shows the top-$k$ precision and recall of three systems on the Spider dataset. We use this dataset mainly to demonstrate that for PK/FK detection within each independent database, the embedding measure alone (\mbox{\sc WarpGate}\xspace) can compare favorably against the ensemble approach (D$^{3}$L) and outperform the syntactic-only approach (Aurum) by a large margin. As to D$^{3}$L, the jump of recall from $k=5$ to $k=10$ is attributed to the column name similarity measure in D$^{3}$L, and we indeed observe in Spider that many PK/FKs share syntactically similar column names. Since Spider has a small number of tables that contain thousands of rows on average, the search time is fast for all three systems, which is no more than 2 seconds for running all the queries.

\subsubsection{Ad-Hoc Discovery in Sigma}
  Since Sigma Sample Database is a corpus without ground truth, we ask four colleagues to pick columns of interest as queries. Here we describe one of the most interesting discoveries. In future work, we plan to conduct a user study to evaluate the value of \mbox{\sc WarpGate}\xspace in a principled manner.

  Continuing our running example of the business user Joey, who is interested in looking for additional information to help make the decision of customer selection. Figure~\ref{fig:interface_functionality} displays the top-3 recommendations from \mbox{\sc WarpGate}\xspace. The first candidate is the \code{Company} column of the \code{LEAD} table from the same database as the query column, which contains information on contact points in each company (e.g., name, title, and address of each contact point). Upon browsing columns in the \code{LEAD} table, Joey does not find auxiliary information for her need. She moves to inspect the second candidate, which is the \code{Company Name} column of the \code{INDUSTRIES} table from the \code{STOCKS} database. In the same table, the \code{Industry Group} column has sector information of US public companies, which can (partially) enrich the query table. Joey then acts on her customer selection by filtering companies with their sectors. Even more interestingly, she can add to the query table the \code{TICKER} column from the \code{INDUSTRIES} table, which can be further used as the join key to add columns of stock prices from the same \code{STOCKS} database. This way, Joey can track down high-performing companies in targeted sectors as her sales campaign customers.

\subsection{Sample Efficiency}\label{subsec:sample_efficiency}
  We run \mbox{\sc WarpGate}\xspace over Nextia\textsubscript{JD}\xspace-S and Nextia\textsubscript{JD}\xspace-M with sample size 10, 100, and 1000. For all sample sizes, the embedding approach remains as effective as using full values with $\pm 1\% / 2\%$ variation at different $k$ values. In the meanwhile, index lookup time is reduced by up to two orders of magnitude (e.g., from about 1 second to about 10 milliseconds on Nextia\textsubscript{JD}\xspace-S), and end-to-end query response time also comes down to interactive speed (under 35 milliseconds per query on Nextia\textsubscript{JD}\xspace-S and under 65 milliseconds per query on Nextia\textsubscript{JD}\xspace-M). We discuss further optimization opportunities in Section~\ref{sec:discussion}.

  To see if sample efficiency is unique to Web Table Embeddings, we also evaluate BERT~\cite{DBLP:conf/naacl/DevlinCLT19} as our underlying embedding model. We observe that the effectiveness of BERT Embeddings is robust to sample size as well. Although BERT is a significantly more complex model, we do not see much improvement in effectiveness (mostly on par with Web Table Embeddings). Nevertheless, index lookup time and query response time are 10x slower without sampling due to more expensive embedding inference costs.
\section{Problem Definition}\label{sec:prob_def}
  In this section, we first briefly introduce Sigma and describe the data discovery needs of Sigma users. Then we formally define our problem.

\subsection{Data Discovery Need in Sigma}
  Sigma is a web-based SaaS platform for cloud data analytics and business intelligence. It features Sigma Workbooks, which provides a spreadsheet-like interface for business users to perform interactive visual analysis of data in CDWs.

  Among many features familiar to business users who are more skilled in spreadsheet functions than SQL, \code{Lookup} enables users to write a spreadsheet formula and add a column from another data source (i.e., another table) to enrich the dataset at hand. Under the hood, Workbooks translates spreadsheet formulas into SQL queries required to fetch data from cloud databases and performs cardinality-preserving joins on related tables.

   A major limitation of \code{Lookup} is that users have to specify the join path in formulas. In other words, it requires users to have prior knowledge about relationships between datasets. However, users often lack such knowledge and have to rely on their data teams for help or manually browse tables in databases to find a clue. This limitation has become a bottleneck in fully utilizing the \code{Lookup} functionality, significantly delaying their data exploration and analysis workflows. We believe this sort of data discovery will be a challenge for all cloud analytics products and hence has to be addressed.

\subsection{Semantic Join Discovery}
  \begin{ps}[Top-$k$ Semantic Join Discovery]
    Given a corpus of tables $\mathcal{S}$, a query column $\mathbf{c_{q}}$ from a table $\mathbf{Q}$, and a constant $k$, find up to $k$ candidate columns from $\mathcal{S}$ such that they are most likely to be joinable with $\mathbf{c_{q}}$.
  \end{ps}

  Traditionally, joinable tables have been discovered through syntactic analysis, which can miss many join opportunities, particularly when data are independently sourced. High dimensional embeddings have recently been shown to capture semantics effectively in many domains~\cite{DBLP:journals/pvldb/DengSL0020, DBLP:journals/pvldb/ZhangSLHDT20}. We expect that the semantic similarity of two columns can be quantified by some similarity measure between their embeddings, which we expect can serve as a proxy for their join-ability as well.

  \begin{definition}[Semantic Column Join-ability]\label{def: sem_col_join}
    Given two columns $A$ and $B$, we define semantic column join-ability $\mathcal{J}(A, B)$ as $$\mathcal{J}(A, B) = \mathcal{M}(\mathcal{T}(A), \mathcal{T}(B))$$ where $\mathcal{T}(\cdot)$ is an embedding function that projects a given column into a vector space and $\mathcal{M}(\cdot, \cdot)$ is a similarity measure between two embeddings in the vector space.
  \end{definition}

  Based on this metric, we can attempt to solve the semantic join discovery problem by returning candidate tables in descending order of semantic column join-ability $\mathcal{J}$ relative to the query column $\mathbf{c_{q}}$. How to do this effectively is the central challenge addressed in this paper.

\section{WarpGate\xspace Overview}
  \begin{figure}[h!t]
    \centering
    \includegraphics[width=0.9\linewidth]{figures/warpgate_pipeline.pdf}
    \caption{\mbox{\sc WarpGate}\xspace pipeline.}
    \label{fig:warpgate_pipeline}
  \end{figure}

  We discuss key challenges to implementing an embedding-based approach to the semantic join discovery problem, along with our solutions to these challenges in Section~\ref{subsec:warpgate_prototype}. Then we describe our system prototype \mbox{\sc WarpGate}\xspace incorporating these ideas in Section~\ref{subsec:warpgate_interface}.

\subsection{Embedding-Based Solution}\label{subsec:warpgate_prototype}

  Figure~\ref{fig:warpgate_pipeline} gives an overview of the \mbox{\sc WarpGate}\xspace architecture, which consists of two main pipelines: indexing and search, which we consider in turn below. But first, we discuss the embedding step common to both pipelines.

\subsubsection{Column Embeddings}
  We consider embeddings at the column level an effective encapsulation of semantics for join discovery. Intuitively, embeddings can capture semantic relationships between columns across databases where value formatting may vary widely despite semantic proximity.

  However, the effectiveness of embeddings depends on the embedding model. We consider several factors in choosing an embedding model:
  \begin{enumerate}[label=(\roman*)]
    \item Whether the model is (pre-)trained over tabular data;
    \item The size of the training corpus;
    \item The efficiency of model inference.
  \end{enumerate}

  We observe two primary paradigms in applying embeddings for data management tasks. Early work ~\cite{DBLP:conf/sigmod/BordawekarS17} leverages off-the-shelf embedding models from the natural language processing (NLP) field. Recent work~\cite{DBLP:conf/sigmod/0002TGL21, DBLP:journals/pvldb/DengSL0020} adapts NLP model architectures and training algorithms for tabular data. As demonstrated in their work, embeddings from models trained on tabular data manifest better performance in downstream tasks. For instance, using the same training algorithm, ~\cite{DBLP:conf/sigmod/0002TGL21} shows that models trained on sequences extracted from tables outperform the model trained on an unstructured text corpus. The performance uplift is attributed to the serialization of tabular data, which considers the table structure in the embedding process. With this observation, we prefer embedding models designed for tabular data.

  Empirically, more training data usually lead to better model performance and generalizability, given enough model capacity. In the database community, Web table corpora~\cite{DBLP:conf/ssdbm/EberiusTBL15, DBLP:conf/www/LehmbergRMB16} extracted from the Common Crawl contribute the largest publicly accessible relational table datasets. Efforts exploring representation learning for relational tables~\cite{DBLP:conf/sigmod/0002TGL21, DBLP:journals/pvldb/DengSL0020} have trained embeddings on various Web table corpus, which also exhibits the impact of Web tables beyond Web applications. Thus, we favor embedding models derived from those large table corpora.

  The embedding process for tabular data generally involves table serialization, input encoding (e.g., tokenization and transformation), and model inference. It is worth noting that although more complex models (in terms of the number of parameters) give embeddings that tend to yield better results in the downstream tasks, they also carry a larger inference cost (i.e., longer inference time, which directly affects query response time) compared to simple models. For interactive applications such as join discovery and recommendation in Sigma, we need to choose models that balance the embedding quality and model inference time.

\subsubsection{Indexing}
  We employ locality-sensitive hashing (LSH)~\cite{DBLP:conf/vldb/GionisIM99, DBLP:conf/stoc/Charikar02} to turn the high-dimensional embedding similarity search problem into nearest neighbor search in a low-dimensional space. The basic idea of LSH is to maximize hash collisions for similar inputs. With respect to a similarity measure, LSH applies a specific family of hash functions that assign two similar inputs to the same ``buckets'' with high probability that is equal to the similarity score between them.

  In our case, we consider the semantic join-ability of two columns to be the cosine similarity of their embeddings. To approximate the cosine similarity of two vectors, we rely on SimHash (also known as random projection)~\cite{DBLP:conf/stoc/Charikar02} that essentially uses hyperplanes in the vector space as hash functions. We hash all column embeddings from the table corpus into a SimHash LSH index. When a query comes in, the LSH index hashes the query embedding and only searches within the sub-universe of embedding vectors (much smaller than the entire universe) that share the same hash as the query, hence reducing the search time.

\subsubsection{Sampling}
  Existing data discovery systems~\cite{DBLP:conf/icde/FernandezAKYMS18, DBLP:conf/icde/BogatuFP020, DBLP:conf/edbt/BogatuPDF22} run a one-pass data scanning step to read all datasets once and retrieve profiling information (e.g., MinHash for each column). Reading full tables of millions of rows or even billions of rows from CDWs is expensive in terms of both computation time and monetary cost because CDW vendors that adopt a usage-based pricing model (i.e., pay-as-you-go) charge per GB of data scanned. It is then intuitive to use sampling to reduce the cost. But one side effect of sampling is potential disruptions to column profiles. For instance, profiling methods like MinHash are shown to be sensitive to the sample size~\cite{DBLP:journals/pvldb/ChenNC14}. It also remains unclear what a good sample size is for embeddings to preserve the similarity for data discovery.

  \begin{figure*}[h!t]
    \centering
    \includegraphics[width=0.95\linewidth]{figures/lookup_interface.png}
    \caption{\mbox{\sc WarpGate}\xspace is integrated into a functionality called \code{Add column via lookup} in Sigma Workbooks.}
    \label{fig:interface_functionality}
  \end{figure*}

\subsection{WarpGate\xspace Interface}\label{subsec:warpgate_interface}
  We have implemented the ideas described above in a prototype system we call \mbox{\sc WarpGate}\xspace. As shown in Figure~\ref{fig:interface_functionality}, we integrate \mbox{\sc WarpGate}\xspace into Sigma Workbooks as a functionality called \code{Add column via lookup}.

  Continuing the example of a business user Joey who wants to identify customers for a sales campaign, she opens up the \code{Account} table and sees a full list of companies (the \code{Name} column highlighted in Step 1 of Figure~\ref{fig:interface_functionality}). To help make the decision, she wants to augment the table with additional information so that she can know which companies fall into the scope of the sale campaign. In the current interface, she right-clicks on the \code{Name} column and chooses the \code{Add column via lookup} option in the menu. A window shows up (Step 2 in Figure~\ref{fig:interface_functionality}) and displays top-$k$ join path recommendations. Each recommendation gives the candidate join column, the table, and the database from which the candidate column comes, as well as a similarity score relative to the \code{Name} column (i.e., the query column). Although recommendations are ranked in descending order of the similarity score, Joey can pick one that fits her interest most. Once she chooses a candidate, the window will display a list of all the columns from the candidate table. Joey can browse and select the most relevant columns. For now, the interface will add user-selected columns to the \code{Account} table right at the side of the \code{Name} column for information complement and also load the candidate table into Sigma Workbooks for further inspection. In Step 3, we simply add and show the first recommended column, which is a foreign key in the same database.

  The current interface design is based on the consideration that it is less desirable to simply join two wide tables with many columns and overwhelm users with a display of an even wider table that does not really help users with their information needs. In this case, Joey can first add the most relevant columns at first glance based on her domain knowledge while having the flexibility to see the entire candidate table for more careful examination.

\section{Discussion}\label{sec:discussion}
\subsection{Sigma Customer Data Scale} The scale of enterprise CDWs is a challenge for any deployment of join discovery. The median Sigma customer warehouse has 450 tables for analysis, but the average is over 12,700 tables with an average of 25.7 columns per table. The size of enterprise tables is also a challenge. The median table in these warehouses has just 7,700 rows, but the average is 1.7 billion rows. Actively sampling this large number of tables incurs usage costs in CDWs, so a passive sampling of user queries is beneficial. Likewise, samples can be shared across ML applications in Sigma to amortize their cost.

\vspace{-1em}
\subsection{Optimization Opportunities}
  \subsubsection{Contextual Embeddings.} Existing approaches generally follow a two-step process: they first build profiles for each column independently and then determine join-ability of two columns based on some notion of similarity between their profiles (e.g., containment or Jaccard similarity). Typically, the context of a column is not taken into account when building those profiles. However, context (e.g., other columns in the same table, user activities, query logs) can potentially provide auxiliary information that is critical to find semantically related candidates. We plan to explore the option of incorporating context information into the underlying embedding model of \mbox{\sc WarpGate}\xspace.

  \vspace{-1em}
  \subsubsection{Data Management for Data Discovery.} CSV is a common file format for tabular data in Open Data Lakes and table repositories on the Web. Despite the flexibility and human readability of CSV files, the flat-file format is not ideal for storage and query purposes. We note that (1) loading giant CSV files and running embedding inference can incur heavy memory usage and crash programs on small machines; (2) operations in join discovery like embedding inference are column-oriented; (3) what is unique in our scenario is that our data are pulled from CDWs, thus more structured and much better data quality than those in the wild. (1) and (2) make us think it is more memory and storage efficient to take advantage of (in-memory) column stores for data pulled out of CDWs, and (3) can make the process easier. This change will also have significant implications for deployment as we need to provision CPU and memory usage in Sigma's Kubernetes cluster.

  \vspace{-1em}
  \subsubsection{Efficient Search.} To further improve query response time for Sigma customers with a massive number of tables in their CDWs, we are considering two directions: (1) a block-and-verify strategy in ~\cite{DBLP:conf/icde/DongT0O21}, which employs pivot-based filtering to prune dissimilar vectors; (2) fine-tune off-the-shelf embedding models in a self-supervised way that pushes embeddings of joinable columns to have higher cosine similarity so that an index data structure like SimHash can be better utilized~\cite{cong2022pylon}.
\section{Introduction}
  Three major correlated forces act on enterprise data analysis today. First, more and more enterprise data are stored on the cloud. Second, the size and the diversity of data available to enterprises increase faster than ever as a growing number of data sources feed into enterprises' cloud data warehouses (CDWs). Third, the number of users in enterprises who would like direct access to cloud data for decision-making is also rapidly growing, boosting the demand for easy-to-use cloud data analysis systems such as Sigma Workbooks~\cite{DBLP:journals/pvldb/GaleSUFWD22}. Hence enabling easy and effective data discovery is increasingly more valuable. However, existing commercial solutions such as data catalogs are typically inaccessible from business intelligence tools, inadequate for surfacing relationships unknown to users, and inflexible in capturing data semantics.

  Not surprisingly, recent years have seen a rising interest in data discovery, especially for Open Data~\cite{DBLP:journals/debu/MillerNZCPA18, DBLP:journals/pvldb/NargesianZPM18, DBLP:conf/sigmod/ZhuDNM19, DBLP:conf/icde/BogatuFP020}. This is a challenging problem due to the unstructured/semi-structured nature of Open Data and the lack of high-quality metadata. However, we observe that modern organizations also face data discovery challenges when dealing with structured data (i.e., databases in CDWs) for similar reasons: (i) in large CDWs, few people have a good understanding of data and their relationships; (ii) data is added in a much less modeled state as cloud computing power and storage become easily accessible; and (iii) business users who do not have comprehensive knowledge of underlying schema designs, are usually unaware of relationships between datasets (e.g., PK/FK relationship between two tables). There is a need for surfacing such relationships to help users connect related datasets within and across databases.
  We also observe that CDW data tends to have more structure than Open Data, sometimes has high update rates, and usually has far more stringent requirements for completeness of discovery results.

  Consider a business user Joey who has access to a CDW that comprises hundreds of databases and thousands of tables (Figure~\ref{fig:sigma_workbook_interface}). In the interest of launching a sales campaign and finding potential customers, she starts with the \code{ACCOUNT} table in their \code{SALESFORCE} database, which contains member and billing data of their customer companies. To select ideal customers for the campaign, she wants to augment the \code{ACCOUNT} table with additional information such as sectors in which companies are engaged in their business activities. Now she has two options to find that complementary information: either she manually browses tables in the same database (even wanders through the entire data warehouse), or she can ask internal data teams for support. Either way, her workflow is significantly delayed. There is also a fair chance that she cannot go through all databases as it is a tedious and laborious process, and the data team does not have that particular piece of knowledge.

  Despite efforts in prototyping data discovery for real-world applications~\cite{DBLP:conf/icde/FernandezAKYMS18, DBLP:journals/pvldb/BharadwajGBG21, DBLP:conf/edbt/BogatuPDF22}, aspects of data discovery pertaining to enterprise settings are overlooked. For example, previous works assume a full pass of underlying data in the indexing/profiling phase. However, it is computationally and monetarily expensive to pull all data out of CDWs. In addition, existing solutions, including those for Open Data, focus on improving index lookup time for the scale of Open Data lakes or Web table corpora, which usually consist of a very large number (million scale) of small tables. In contrast, we observe that Sigma customers' CDWs have several thousand tables on average but can have billions of rows. Due to the relatively smaller number of tables but larger table sizes, index lookup time becomes less dominant compared to data loading time and profiling time, both of which can now bottleneck end-to-end query response time in the pipeline. So, the main challenge in the Sigma scenario shifts in part from searching over a massive number of small tables to optimizing the entire pipeline for a fair number of very large tables (e.g., reducing data loading time and using efficient profiling methods).

  In this work, we introduce a data discovery system \mbox{\sc WarpGate}\xspace and share our progress. \mbox{\sc WarpGate}\xspace prototypes semantic join discovery in Sigma, an enterprise platform for cloud data analytics. Specifically, we make the following contributions:
  \begin{itemize}
    \item We present \mbox{\sc WarpGate}\xspace, a semantic join discovery system, which demonstrates a real-world usage scenario of data discovery within an enterprise product.
    \item We identify unique challenges in Sigma and propose an embedding-based solution to capture semantic relationships between tables, especially those across databases.
    \item We evaluate \mbox{\sc WarpGate}\xspace on three cross-domain table corpora, including one collected from a CDW, and show our embedding-based approach is effective and robust to sampling. We also discuss further optimization opportunities for deployment and larger-scale discovery.
  \end{itemize}

\section{Related Work}\label{sec:related_work}
 \mbox{\sc WarpGate}\xspace builds on earlier work on data discovery for enterprise data systems and Open Data, which we summarize below. We distinguish \mbox{\sc WarpGate}\xspace from this earlier work mainly in two aspects: (i) we recognize and showcase the value of data discovery for structured data in enterprise CDWs; (ii) we do not assume a full pass of data in the indexing/profiling phase and show that the embedding-based approach is effective and robust to data samples.

\subhead{Data Discovery Systems for Enterprise.} Aurum~\cite{DBLP:conf/icde/FernandezAKYMS18}  models syntactic relationships between datasets in a graph data structure to support data discovery. In a two-step process of profiling data and computing the relationship between data signatures, Aurum builds up a graph with nodes representing column profiles and weighted edges indicating syntactic relationships between two nodes (e.g., content similarity). With an in-memory index of the graph, Aurum can efficiently support various discovery needs, such as how similar two columns are, which can be used to find joinable datasets.
 SemProp~\cite{DBLP:conf/icde/FernandezMQEIMO18} extends Aurum by using word embeddings to surface semantically related objects in the graph.

  Voyager~\cite{DBLP:conf/edbt/BogatuPDF22} built on top of D$^{3}$L~\cite{DBLP:conf/icde/BogatuFP020} formalizes and supports three tasks routinely performed by data scientists using their platform. It profiles data with a full scan and indexes columns with MinHash. Voyager also drops the embedding measure in D$^{3}$L and relies only on syntactic measures.

\subhead{Join Discovery over Open Data.} JOSIE~\cite{DBLP:conf/sigmod/ZhuDNM19} considers the problem of finding joinable tables in modern data lakes. It formulates the problem as a top-k overlap set similarity search problem where columns are treated as sets and matching values as intersections between sets. By using a cost model for estimating the reading costs of data structures, JOSIE contributes an exact overlap set similarity search algorithm that can adapt to different data distributions.

  Juneu~\cite{DBLP:conf/sigmod/ZhangI20} aims to help users find semantically related datasets in data lakes to augment data analytics tasks, including finding joinable tables in interactive data science environments (e.g., Jupyter Notebook). It combines multiple table-relatedness measures such as column and row overlap, provenance, and user context and intent for related-table search. In a similar vein, D$^{3}$L~\cite{DBLP:conf/icde/BogatuFP020} uses five types of evidence to determine column proximity: (i) column name similarity; (ii) column extent overlap; (iii) word-embedding similarity; (iv) format representation similarity; and (v) domain distribution similarity for numerical attributes. This ensemble approach is shown to be more effective and efficient than Aurum on table union search, which is a problem that has significant overlap with join path discovery.

  In contrast to the search paradigm, Nargesian et al.~\cite{DBLP:conf/sigmod/NargesianPZBM20} propose building a navigation graph over data lakes. By leveraging a probabilistic model, they show that navigation can help users find relevant tables that cannot be found by keyword search.

redpajama_set_name: RedPajamaArXiv


Scott Millichamp is Co-Principal Horn of the Midland-Odessa Symphony& Chorale, Fourth Horn with Abilene Philharmonic, Second Horn with the Missouri Symphony summer arts festival, Instructor of Theory and Aural Skills at University of Texas Permian Basin, and Co-Music Director at Unitarian Universalist Church of Midland. A native of Detroit,he graduated from Interlochen Arts Academy and received his Bachelor and Master degrees in Horn Performance from Indiana University. Following his time at IU, he served concurrently as a Graduate Teaching Assistant in Composition at the University of Hawaii in Manoa, a substitute hornist with the Honolulu Symphony Orchestra, pianist for Honolulu Waldorf School eurythmics classes, andChoir Accompanist for First Unitarian Universalist Church of Honolulu. Prior to his MOS Cappointment in 2009, he served as Fourth Horn of the Arkansas Symphony Orchestra; he has also performed with Arkansas's Opera in the Ozarks festival and the Lyrique-en-Mer opera festival on Belle-Ile, France.


This is Michael's FIRST Legends Of S.H.I.E.L.D. discussion appearance.
Runaways "Metamorphosis" aired on Hulu on December 19th, 2017.
4th Discussion Appearance on Legends Of S.H.I.E.L.D.!
-The blockbuster has generated $358 million in North America, along with $645 million overseas.
Why are there 2 Captain Marvels?
@LegendsofSHIELD I doubt you have noticed, but Marvel restricts vendors from displaying pictures of figures related to a movie until after the release.
This podcast was recorded on Sunday April 7th, 2019.
#ListenUpAHoles @LegendsofSHIELD There is a MCU character in this list!
@LegendsofSHIELD listening to episode 231, SP sounds like a member of the I.O.A.
@LegendsofSHIELD @StargatePioneer @sithwitch What are your feeling about the return of the Uncanny X-Men/X-Men Black? Have you read any of the one-offs leading up to it?
This podcast was recorded on Sunday November 4th, 2018.
The pink slipping of the Mike Colter-led series was ultimately a combo of the age old creative differences and the inability for the parties involved to reach a deal, according to sources. We hear that Netflix was not particularly impressed by the delivered Season 3 scripts. The writers room was recently put on hold, and we hear a behind-the-scenes turmoil ensued this week. There was a disagreement between the sides on a creative direction and possible personnel changes, leading to the cancelation.
Tom Holland has officially revealed what appears to be his main costume in Spider-Man: Far From Home, the sequel to 2017's Marvel/Sony cinematic collaboration, Spider-Man: Homecoming. It all took place on the October 18 edition of Jimmy Kimmel Live. During the host's opening monologue, the head of his security detail, Guillermo (Rodriguez), ran out wearing what appeared to be a movie-accurate Spider-Man mask.
This podcast was recorded on Sunday October 21th, 2018.


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Home Tags UP government
Tag: UP government
BSP will probe Akhilesh's 'big' economic decisions: Mayawati
JKR Staff - October 28, 2016
BSP president Mayawati on Friday said she would order a probe into the various economic decisions taken by chief minister Akhilesh Yadav in the...
"मेरे तीन बच्‍चे दिनभर पत्‍थर तोड़ते हैं, तब कहीं जाकर हमें...
शामली और कैराना पर राष्ट्रीय मानवाधिकार आयोग ने जो रिर्पोट दी है उसकी राष्ट्रीय अल्पसंख्यक आयोग कड़ी आलोचना की है। शामली और कैराना से...
Vishwa Hindu Parishad leader caught molesting Muslim devotees wearing burqa during...
A Vishwa Hindu Parishad (VHP) activist, along with his business associate, was thrashed and handed over to the police for allegedly donning a burqa...
Muzaffarnagar riots victim robbed of cash, valuables
JKR Staff - September 30, 2016
A victim of the 2013 Muzaffarnagar riots was allegedly robbed of cash and valuables by a gang of six armed men in Shamli district,...
No more 'janata darshan' in future, says Akhilesh Yadav
PTI - September 29, 2016
Uttar Pradesh chief minister Akhilesh Yadav on Wednesday announced there will be no 'Janata darshan', a platform where he used to hear grievances of...
Rahul Gandhi accuses UP ministers of "patronising" criminals
Congress vice president Rahul Gandhi on Wednesday accused the ministers in the Akhilesh Yadav government in Uttar Pradesh of "patronising" criminals, saying this was...
UP: Three killed in communal clash in Bijnore
Three persons were killed and at least 12 injured on Friday in a clash between two communities in Bijnore after a school girl was allegedly...
BJP questions UP government on photos of police torching vehicles
Janta Ka Reporter - October 8, 2015
The BJP on Wednesday sought an explanation from the Uttar Pradesh government after photographs of policemen torching vehicles in Varanasi emerged in the media. Seeking...


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TURBINIA
HOME | DONATE | EVENTS | INDEX | ROBOTICS
Turbinia full steam ahead in 1897
Turbinia was the first steam turbine powered ship. Built as an experimental vessel in 1894, and easily the fastest ship in the world at that time, Turbinia was demonstrated dramatically at the Spithead Navy Review in 1897 and set the standard for the next generation of steamships, the majority of which would be turbine powered. The vessel is currently located at the Discovery Museum in Newcastle upon Tyne, North East England, while her original powerplant is located at the London Science Museum.
Charles Algernon Parsons invented the modern steam turbine in 1884, and, having foreseen its potential to power ships, he set up the Parsons Marine Steam Turbine Company with five associates in 1893. To develop this he had the experimental vessel Turbinia built in a light design of steel by the firm of Brown and Hood, based at Wallsend on Tyne in the Northeast of England.
The Admiralty was kept informed of developments, and Turbinia was launched on 2 August 1894. Despite the success of the turbine engine, initial trials with one propeller were disappointing. After discovering the problem of cavitation and constructing the first cavitation tunnel, Parsons' research led to his fitting three axial-flow turbines to three shafts, each shaft in turn driving three propellers, giving a total of nine propellers. In trials this achieved a top speed of over 34 knots (63 km/h), so that "the passengers aboard would be convinced beyond all doubt Turbinia was Charles Parsons' winning North Sea greyhound".
The turbines were directly driven, as geared turbines were not introduced until 1910. Even after the introduction of geared turbines, efficiency of even the largest axial steam turbines was still below 12 percent. Turbinia was even less efficient, with its direct drive turbine moving with a top speed of just 30 meters per second. Despite this, it was a dramatic improvement over predecessors.
Queen Victoria's Spithead Review in 1897, with Turbinia making an entrance.
SPITHEAD REVIEW 1897
Parsons' ship turned up unannounced at the Navy Review for Queen Victoria's Diamond Jubilee at Spithead, on 26 June 1897, in front of the Prince of Wales, Lords of the Admiralty and foreign dignitaries. As an audacious publicity stunt, Turbinia, which was much faster than any ship at the time, raced between the two lines of navy ships and steamed up and down in front of the crowd and princes, while easily evading a Navy picket boat that tried to pursue her, almost swamping it with her wake.
Photographer and cinematographer Alfred J. West took several photographs of Turbinia traveling at full speed at the Review. He was subsequently invited by Sir Charles Parsons to film and photograph the vessel within the River Tyne and the adjacent North Sea and the pictures captured (one of which is shown at the top of this page) remain the defining image of Turbinia at speed.
From this clear demonstration of her speed and power and after further high speed trials attended by the Admiralty, Parsons set up the Turbinia Works at Wallsend, which then constructed the engines for two turbine-powered destroyers for the Navy, HMS Viper and HMS Cobra, which were launched in 1899. Both vessels were later lost but although the loss of these trial ships slowed the introduction of turbines, the Admiralty had been convinced. In 1900 Turbinia steamed to Paris and was shown to French officials and then displayed at the Paris Exhibition.
The first turbine-powered merchant vessel, the Clyde steamer TS King Edward, followed in 1901. The Admiralty confirmed in 1905 that all future Royal Navy vessels were to be turbine-powered, and in 1906 the first turbine-powered battleship, the revolutionary HMS Dreadnought, was launched.
CROSBY LAUNCH
On 11 January 1907, Turbinia was struck and nearly cut in two by Crosby – a ship being launched across-river from the south bank of the Tyne. She was repaired (see accompanying image) and steamed alongside RMS Mauretania (also a turbine-powered vessel) after the launch of the great ocean liner. However, mechanical problems prevented Turbinia from accompanying Mauretania down the River Tyne to the sea.
MUSEUM SHIP
The company decided to slow down the deterioration of Turbinia by lifting her out of the water in 1908, and in 1926 the directors of the Parsons Marine Steam Turbine Company offered the ship to the London Science Museum. Turbinia was cut in two and the aft section, complete with engines and propellers, was put on display in the South Kensington museum in London, which did not have the space to accommodate the full ship. The fore section was presented in 1944 to Newcastle Corporation and placed on display in the city's Exhibition Park. In 1959, the Science Museum took the aft section of Turbinia off display and by 1961, using a reconstructed centre section, Turbinia was once more complete and on display in the Newcastle Municipal Museum of Science and Industry. In 1983 a complete reconstruction was undertaken.
On 30 October 1994, 100 years after her launch, Turbinia was moved to Newcastle's Museum of Science and Engineering (later renamed The Discovery Museum) and put on display to the public in March 1996. Listed as part of the National Historic Fleet, in 2000, the vessel was the focal point of a year-long £10.7 million redevelopment programme at the Discovery Museum. The gallery around Turbinia was the first area to be refurbished, with the main part of the work involving raising the roof by one storey to create viewing galleries on three levels.
Turbinia in dry dock for repairs after being almost cut in half
The Turbinia is seen here docked alongside the Muaretania
https://en.wikipedia.org/wiki/Turbinia
This website is Copyright © 2019 Bluebird Marine Systems Limited. The name Bluebird™ and bird logo are registered trademarks.
INVENTOR OF THE STEAM TURBINE ENGINE, TURBINIA


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Home Cannabis Texas Continues To Take Baby Steps Towards Legalization
Texas Continues To Take Baby Steps Towards Legalization
By: Green Market Report
CannabisGreen Market ReportMarijuana LegislationNews
Photo by adamkaz/Getty Images
The Texas Tribune reported on Monday that the House of Representatives approved a bill that would decriminalize possession of cannabis. Rep. Joseph Moody (D), the chief sponsor of the bill, still had to amend it in order to get the bill approved. His original version had a lower fine of $250 and it would have dropped down low-level possession to a civil infraction instead of a class C misdemeanor.
Still, the paper reported that the chances of the bill turning into law would be slim. "After the House grants final approval for the bill — usually just a formality — it will head to the Senate, where presiding officer Lt. Gov. Dan Patrick has expressed opposition to the idea of loosening marijuana possession penalties." The resistance is attributed to the fear that each move to loosen cannabis laws is another step towards full legalization.
RELATED: How Texas Cannabis Activists Are Battling Stoner Stereotypes
If Texas manages to pass the law, it would become the 25th state to decriminalize cannabis. "Texans have suffered under failing marijuana policies for far too long," Heather Fazio, director of Texans for Responsible Marijuana Policy, said. "Rep. Moody's bill will help preserve valuable public safety resources and keep a marijuana charge from derailing someone's life. Like a majority of Texas voters, Democratic and Republican lawmakers agree that marijuana laws need to change."
Medical marijuana is legal in the state but on a very restrictive level. There is only one approved condition, which is intractable epilepsy. Several pieces of legislation have been filed in order to expand the condition list.
Hemp Farming Legalized
Just last week, The Texas House approved a bill that would allow farmers in the state to legally grow industrial hemp. House Bill 1325 from state Rep. Tracy King, D-Batesville, would also legalize hemp and hemp-derived extracts like CBD oil as long as they contain no more than 0.3% THC. "HB 1325 is right-to-farm legislation that will allow Texas farmers the opportunity to cultivate a drought-resistant cash crop — that being hemp," King told other House members.
The bill has now headed to the Senate for approval. Texas Agriculture CommissionerSid Miller is a strong supporter of industrial hemp production as a new market opportunity for Texas farmers to expand their operations and grow alternative crops. Once authorized by the Legislature, TDA said it will create rules regarding licensing, production, testing, seed certification and other oversight as necessary.
"There's no good reason for Texas farmers and ranchers not to have hemp as a crop option," said Gene Hall, a spokesman for the Texas Farm Bureau. "I suspect a lot of farmers will choose this option once it's available. It's a drought-tolerant crop and can be grown anywhere where cropping is prevalent right now."
This article originally appeared on Green Market Report.
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5 Questions About Champagne You Always Wanted Answered
Zach Geballe - December 31, 2019
Champagne is one of life's great delights. Unfortunately, it tends to get relegated to toasts and special occasions in America, much to my dismay.
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Seagate has released the new GoFlex drive series, and to make them more appealing, it's offering you the chance to take full use of the USB 3.0 speeds.
USB 3.0 mighht be the next big thing in data tranfer, with speeds theoretically 3 times highr than USB 2.0, but it's still such a new technology that Seagate couldn't incorporate it in the new GoFlex external drives. But it's done something almost as good. The company is now selling a smart kit that will turn any laptop's Express Slot into a USB 3.0 port for one of its GoFlex drives.
To experience the unbelieveable speeds of USB 3.0, all you have to do is connect an adapter to the GoFlex drive, and then plug it into one of your laptop's USB 2.0 slots. It may not be the most comfortable, or good looking solutions, but until something better comes along, it's totally worth the advantages of the new USB 3.0 technology.


Neo PanTyger is a fursuiter from Baton Rouge, Louisiana, United States. He moved to Illinois around 2008 to attend grad school. His fursona is a Giant Panda/Bengal tiger hybrid. He is mated.
He is a member of the Louisiana Furs, and has attended Rocket City FurMeet (his first convention was RCFM 2007) nd Mephit Furmeet. Neo won Best in Show at the 2007 MFM Masquerade for his "Elvis Pantyger" performance. He also performed with Bucktown Tiger.
Neo won the bronze award in the Fandom's Favorite Fursuit Fracas 2008. He won first place in the Fursuit Dance competition at Furry Weekend Atlanta 2009.
Several of Neo's videos have also been featured on the Funday PawPet Show.


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Visas, Travel Health and Safety Tips for New Zealand
Everything you need to know before you go to New Zealand, from Wi-Fi and sand flies to phone coverage and road safety.
Photo © iStock
Before you hop on a plane to New Zealand, there are a few boring things to sort out. Find out what regions are likely to have less phone coverage, how to drive on the roads, and critical vaccines for your trip.
Quarantine laws in New Zealand
Visas for New Zealand
Do I need vaccinations for New Zealand?
Travel health in New Zealand
Crime and security in New Zealand
Travelers hiring cars
Staying connected and phone coverage
New Zealand is place of delicate ecosystems, and the Government do their best to keep it that way. Anyone who fails to declare fresh produce, seeds or pets upon arrival may be handed prison time. Items can, and often are, seized. There are strict import and export regulations, and offenders will be handed a stiff fine.
It's best to check in with the Biosecurity New Zealand website.
If you're bringing worn-in hiking boots that might be dirty, keep them at the top of your bag and take them out to be inspected. The environment here is pristine because of their strict rules, so don't disobey them. It's much easier to delcare anything you think might get you in trouble than have to explain yourself later.
For US citizens, your first three months in New Zealand will not require a visa. You can apply for more time at the border, but no more than six months for any 12-month period. You will need a valid passport.
UK citizens (or passport holders) can stay up to six months without a visa. If you're not a UK or US citizen, you may need a visa. Apply here.
There are no vaccinations required to enter New Zealand. However, you may wish to visit your own travel doctor to seek their professional advice on what's right for you.
Hepatitis A and hepatitis B
Hepatitis A (food and water borne) and hepatitis B (tattooing, manicures, haircuts and dental procedures or any type of medical intervention) immunizations are critical.
Tetanus/Diptheria/Acellular Pertussis
For anyone that's planning to participate in extreme sports, check with your doctor to see if these vaccinations are required.
Though rare, pre-exposure to rabies will insure that if you're in a remote area, you will have up to two days to get two additional immunizations post exposure.
A seasonal influenza vaccine lasts for three–four months. The North Island of New Zealand has influenza year round, but the central and South Island have influenza from April–September.
Pneumonia is very often a consequence of superinfection after being challenged with Influenza. For any person over the age of 50, or who has significant pulmonary risk, immunization with the Pneumococcal vaccine is recommended.
Travel health tips for New Zealand
Sand flies
You might not know this, but sand flies are a real pest in New Zealand – especially on the South Island during summer. Pack bug spray to keep them at bay. Better yet, wear long sleeved clothing to ensure they don't swarm all over you.
It turns out sand flies are attracted to bright colors. So, if your raincoat is bright yellow and your hands aren't covered up, you could find yourself with a case of itchy phalanges.
Drinking water and staying hydrated
Just in case you were wondering, the tap water is safe to drink in New Zealand. If you're going tramping and find yourself in the wilderness with no access to tap water, find the nearest river and fill up your water bottle.
Most travelers advise that they do this frequently in National Parks such as Fiordland or Arthur's Pass. The rivers are usually glacial ice melt, so it's highly unlikely that the water will have been trampled on or contaminated.
The best way to reduce your trash footprint would be to bring along a water purifying bottle. Check out products such as Grayl, for water bottles that can purify on the go.
A diarrhea kit – when used appropriately – can remove the risk of severe electrolyte depletion as a result of diarrhea, vomiting, or excessive fluid loss due to sweating. This could come in super handy if you're planning to head out for a lengthy tramping trip, but got food poisoning (not a common occurrence in New Zealand, but you never know).
Jet lag and high altitudes
If you're attempting to climb any of New Zealand's highest peaks – namely Mount Cook – be sure to know the symptoms of altitude sickness and take measures to acclimatize appropriately, and research your tour operator before making a selection.
If you've just hopped off a long-haul flight, the first day probably isn't going to be the best time to start the Routeburn Track. Give yourself a few days to get through the jet lag. We asked a few flight professionals to weigh in on their top tips, you can read them here.
If you're traveling to New Zealand for elective surgery, check out online reviews of your physician's credentials, and facilities for emergency care.
New Zealand will always lure adventure-junkies to it's landscapes. Always travel with a buddy, and know the closest rescue center and emergency numbers to dial if there's an accident in the wilderness.
Though medical care is high quality, there's a wait for procedures, and bills often must be made at time of service.
The Department of State encourages all US citizens traveling or residing abroad to register via the State Department's travel registration.
Be familiar with the local traffic issues of driving on the left, yielding to traffic on the right, and a complete stop at red lights.
Avoid situations which put you, the motorist, at risk – especially with road conditions even on straightaways. These risks could include unexpected animal crossings, not knowing that cars must stop at least two meters from a crosswalk that is in use, and (our biggest pet-peeve) pulling over on the side of the road to take incredible pictures at very dangerous locations.
There are many one-way bridges on the South Island of New Zealand, which can be very confronting for travelers who've just hired a car and are still getting used to driving on the left-hand side of the road. As you leave the one-way bridge, remember to stick to the left-hand side if there's no on-coming traffic.
New Zealand's roads are often quiet, so exiting these bridges without any cars on the opposite side of the road can be a little daunting for first-timers.
New Zealand is a land of plenty. Rich, creamy dairy products, lamb "fed on lush meadow grass and mother's milk", and the brown, furry kiwifruit (brought over from central China almost a century ago, it was called a Chinese gooseberry and renamed for export) are just a few of the many delicious items New Zealand produces.
Most of the food should be familiar to visitors, New Zealand boasts cuisine from all around the world, as well as traditional New Zealand and Polynesian restaurants. A Maori hangi (feast), featuring Maori specialties steamed to perfection in an underground oven, is an eating experience that shouldn't be missed.
New Zealand's wine has improved dramatically in the last decade. Known for its grassy and aeromatic Sauvignon Blanc, visitors can enjoy hours of fun at various wineries.
Try the New Zealand Pinot Noir as well.
Phone coverage is poor in outlying areas, and is of particular concern to drivers. Try downloading offline maps prior to wilderness driving, and research what areas have less coverage.
When you arrive at the airport, shop around to see if there's a good pre-paid SIM deal that fits your budget. It might be worth purchasing one to stay (somewhat) connected on your trip.
By Amelia Brady,
World Nomads Contributor - Tue, 19 Nov 2019
Get a travel insurance quote for New Zealand
A Guide to Working Holidays in New Zealand
Interested in a working holiday in New Zealand? Find out about the working holiday visa scheme and how to get a visa before you go.
A First-Timers Guide to WWOOFing in New Zealand
Keen to work abroad in New Zealand and don't mind getting your hands dirty? Try WWOOFing. Here's what to expect and how to apply.
Volunteering Overseas Without Breaking the Bank
Volunteering offers among the most immersive experiences that you can get, a front row seat to the realities of life in a given place. Find out how to give more for less.
Download our free Insiders' Guide to New Zealand


Mutual Advantage - make your savings go further, and support charity at the same time. It's philanthropy made easy.
With Mutual Advantage you'll continue to earn interest on your funds and be able to support charity at the same time - without needing to do anything. You're able to invest from £100 to £50,000, make additional investments whenever you wish (up to the maximum balance) and access your funds with three withdrawals per year. If you need additional access this can be done through 30 days' notice or interest penalty.
Postal accounts opened will also be in support of 4YP.
Because we want to keep supporting charities doing vital work in our local communities we'll change the chosen charities each year on 1 July. We'll update you by email to let you know how you can help to choose the new charity for your branch for the year ahead, and how much bonus has been paid to the outgoing organisation.
This savings account not only pays you an interest rate just like any other savings account, but we also give your nominated branch charity a 1% bonus each year based on the average balance of 'their' accounts across the year.
You can open a Mutual Advantage account by visiting a branch or contact us to find out more.


Capricorn's, symbolized by the goat, are ambitious, practical, realistic, and hard working. The problem, however, is that they are unforgiving and can appear to others as a know it all. If you are a Capricorn, you believe in the power of family more than anything else.
If this is your star sign, you are the most compatible with those that are Taurus and Virgo, but you make the best long term relationships with Cancer. Capricorn and Cancer create full and loving families because both signs are adept at working hard at relationships and balance each other with doses of common sense and nurturing tendencies. Capricorn's are devoted to their families and spouses.
With their practicality, Capricorn's are great at saving money and work well at jobs involving finance, education, and real estate. They work hard to get what they want, and do their best to make their work the best quality it can be.
As steady as a rock, Capricorn's will not let you down, though they make take awhile to get where they are going because they believe in cautious actions.


Opublikowane Luty 6, 2019 Luty 7, 2019 przez CaffePosted in Daily NewsOtagowane Antydepresant, Bajt, Pełnia szczęścia, Pies, Plenitude of happiness, Spacerek, Śpioch.
Hahha, love that fluffy yawning creature. My dog gets so excited to go out at any hour, but lately he decided to play a sort of catch me if you can game before we go out. He hides under the bed while wagging his tail, when I call him to put the leash on him he doesn't wanna come out, then I go and sit on the sofa waiting and he comes with that tricky look on his face..Aren't we going out? And the whole process repeats itself until I managed to close the door to the bedroom.
Pets are awesome! Even though a part of me wishes they had more freedom and spent most of their time outdoors. Can't wait to move to a house with a garden in the future, just for the sake of my dog.
Yep, I was the same. I liked the idea of living in a flat in a big, cosmopolitan city. But gosh,now I'd give anythinf for a cozy house with a garden in a quiet and clean neighborhood.
They will. Then we'll post pictures of our happy dogs running wild in the garden.
A to dziwne, bo w moim domu, to pies namawia pana na spacer i zazwyczaj ma problem wywabić go z wyrka. Pozdrawiam.
Myślałem przez chwilę, że to Chewbacca. Cóż, oczy trochę jeszcze łzawią.
Naprawdę trafił nam się śpioch:))) Im starszy tym większy.
Always tired:))) It revives only at the sight of the treat.
Takie wygodne legowisko, a Ty na spacer? Sadystka!