Film review by Kevin Patterson
EYES WIDE SHUT Rating: ***1/2 (out of four) R, 1999 Director: Stanley Kubrick Screenplay: Stanley Kubrick and Frederic Raphael Starring Cast: Tom Cruise, Nicole Kidman, Sydney Pollack
EYES WIDE SHUT arrives in theaters as one of the most anticipated releases of 1999, overshadowed perhaps only by STAR WARS: EPISODE I - THE PHANTOM MENACE. The reason for all the hype, of course, is that it is not only the first film from Stanley Kubrick in thirteen years, but it is also his last, as he died shortly after showing an initial cut to the cast and to studio executives. This situation has led to some controversy over whether or not the film, as released, truly represents Kubrick's intentions for the final product, especially after it became known that digital images were inserted during one scene in order to ensure an R-rating. Adding to the hype has been one report that Kubrick had told a friend that, in his estimation, Eyes Wide Shut was his best film yet.
I don't think that EYES WIDE SHUT is, or could have been, Kubrick's best film yet. It does not reach the heights of DR. STRANGELOVE or 2001: A SPACE ODYSSEY, and I doubt that any post-production tinkering would have changed that. But with all the attention that EYES WIDE SHUT has received, we tend to forget that Kubrick probably never conceived of this film as any sort of "grand finale." He simply set out to make an original and thought-provoking drama, and at that task he has succeeded admirably.
As EYES WIDE SHUT begins, we are introduced to a wealthy New York couple, Dr. Bill Harford (Tom Cruise) and his wife Alice (Nicole Kidman). They go to a party hosted by Victor Ziegler (Sydney Pollack), a patient of Bill's who moves in an even more elite crowd than the Harfords. At the party, both Bill and Alice, after having a few too many drinks each, flirt briefly with others, but nothing comes of it and they go home. The following evening, while the two are smoking pot in their bedroom, Alice asks Bill about the women who were with him at the party. They quarrel over whether or not there is any jealousy present in their relationship, and finally she recounts a story about a handsome naval officer she noticed on their family vacation years ago and claims that, had the officer shown any interest in her, she might well have left Bill and their young daughter for him.
Bill is understandably upset, but before he can resolve things with Alice, he is called away to the home of a patient whose father has just died. After he leaves, he goes out for a walk and encounters a variety of sexual temptations, but never actually acts on any of them. He goes to the apartment of a prostitute, for example, but his cellular phone rings before the two of them do anything. Eventually he makes his way to an isolated mansion, where he finds a strange ritual taking place that seems to be a hybrid between a religious ceremony and a sexual orgy. All the participants are masked, and a secret password is required to enter. A woman approaches Bill and warns him that he is in danger, and soon the masters of ceremonies realize that he is not supposed to be there. It seems inevitable that he will somehow be "punished" and possibly even killed, when suddenly the unidentified woman intervenes on his behalf. He is ordered to leave immediately, and no one will tell him what will become of the woman now.
At times, it seems as though EYES WIDE SHUT is about to settle into the mode of a classic thriller, with a single protagonist on the run from powerful, shadowy forces. Bill has reason to believe that the mysterious woman from the orgy, as well as the acquaintance who illicitly gave him the password, have been killed. When he tries to determine their whereabouts, he is warned in no uncertain terms to stay away, and the safety of his family is threatened. There are a few elements that are disappointingly conventional, such as an ominous figure in a trenchcoat who seems to be tailing Bill, or the two anonymous men who approach him in a black car and step out to deliver him a typewritten note. (There is, however, at least a hint that this stereotypical, clichéd 'bad guy' behavior may have been intentional.)
For the most part, however, Kubrick and co-writer Frederic Raphael keep the focus on the emotional suspense, as Bill and Alice continue to deal with their personal fears and insecurities about themselves and about each other. One of the most interesting aspects of the story is that their relationship nearly falls apart over sexual jealousy despite the fact that neither of them ever actually cheats on the other. Instead, they are driven apart by mere suggestions, possibilities, and even dreams -- one night, Bill comes home to a distraught Alice, who has had a nightmare in which she imagined herself having sex with countless men at once and laughing at Bill as he looked on. And even as he fears for his life, Bill continues to flirt with the New York City sexual underworld, still never actually betraying his wife but coming dangerously close at least once more. The thriller plot is composed similarly out of suggestion and ambiguity, as it is never clear if any real violence has taken place or if the entire situation is just a combination of unusual coincidences and intimidation tactics. The conflicts in EYES WIDE SHUT arise not out of actual events but out of the possibilities that exist in the minds of Alice and Bill, as they contemplate sexual abandon and, in Bill's case, physical danger. Issues of internal moral character become more important than legalistic details of what exactly happened (or, in this case, didn't happen) and why and when.
Kubrick's talent for hypnotic style and masterful orchestration makes him an ideal director for this sort of material, which relies more on thematic coherence and on the intensity of the psychological experiences than on its intentionally flimsy plot. He creates a convincing cinematic landscape, with an emphasis on the bleak and slightly decadent aspects of life in New York City, then utilizing them to construct a nightmarish excursion into the sexual underworld that is at once frightening yet believable. The orgy scene in particular is a perfectly composed piece of surreal tension, as the participants chant in a foreign language and engage in mechanical, ritualistic sex as Bill wanders through, perhaps fascinated, perhaps repulsed, and perhaps both. What little dialogue the scene contains is spoken from behind masks, creating the eerie impression of voices dissociated from the actual persons, floating in a void where ordinary human interactions have no place.
As is typical of Kubrick's films, the characters' quandaries arise out of a form of dehumanization, in this case the possibility that simple, impersonal sex could replace devotion and love as the center of Bill's and Alice's lives. Despite its explicit content, EYES WIDE SHUT is not an exhortation to the mindless sort of "sexual freedom" that permeates Hollywood. (If anything, it gets a little too obviously preachy; a brief subplot involving an HIV scare can't help but come off like something out of a high school sex-ed video.) The scenes of nudity, which are used almost exclusively to suggest infidelity and objectification, are more disturbing than anything else, and are certainly not intended to be erotic or sensational: stylistically, EYES WIDE SHUT has much more in common with BLUE VELVET or EXISTENZ than it does with the latest soft core direct-to-video garbage.
There are times when the psychological drama and the suspense thriller seem to rest awkwardly side-by-side, but Kubrick and Raphael effectively resolve both threads at once in a conclusion that is low-key but appropriate and actually somewhat optimistic. There are no real scenes of confrontation in the final reels of EYES WIDE SHUT. Instead, the threat of physical harm and conspiracy is marginalized to the point where Bill feels he can safely leave behind the strange events of recent days, and he and Alice seem ready to count their blessings and move forward. And yet this is not a picture-perfect ending. There are some lingering doubts in their dialogue in the last scene, and it is not lost on them that they may have been saved partly by coincidence and lack of opportunity.
Kubrick has always been known as a somewhat pessimistic filmmaker, perhaps not without reason: A CLOCKWORK ORANGE imagined a future world overrun by selfishness and violence, DR. STRANGELOVE ended with nuclear holocaust, and the "happy" ending of 2001 showed a character who achieved enlightenment only when he ceased to be human. EYES WIDE SHUT demonstrates that he can play the other side of the coin as well and create characters who are not defeated by the unavoidable dangers of his troubled universe. Bill and Alice don't quite descend all the way into the abyss and climb back out, as is par for the course in a cautionary tale with an optimistic conclusion, but at the very least they step right up to the edge of the abyss and still manage to turn away.
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Thunberg to guest-edit BBC radio news programme
LONDON: Teenage Swedish environmental activist Greta Thunberg will guest-edit BBC radio's main daily current affairs programme in December, the broadcaster announced on Saturday (Nov 23).
The 16-year-old climate change campaigner will be one of five prominent personalities invited to take over the "Today" programme between Christmas and New Year, the British Broadcasting Corporation said.
Thunberg will speak to the world's leading climate change figures and hear from front-line activists, said the BBC.
She has also commissioned reports from the Antarctic and Zambia and an interview with Bank of England governor Mark Carney.
READ: Greta Thunberg - 5 things you need to know about the teen climate activist
READ: Nobel snub no obstacle in Greta Thunberg's climate quest
The other four guest editors on the 6am to 9am daily programme on BBC Radio 4 will be Supreme Court president Brenda Hale, transvestite potter Grayson Perry, poet performer George the Poet and Charles Moore, the former editor of The Daily Telegraph newspaper.
Previous guest editors have included physicist Stephen Hawking, Prince Harry, actor and campaigner Angelina Jolie and Virgin tycoon Richard Branson.
The Today programme often sets the political news agenda for the rest of the day in Britain.


Chat Features
Client Features
The VOLANO chat server includes three Java applets: VolanoChat, MyVolanoChat, and WebVolanoChat. These client applets are written in pure Java to run in any Web browser with Java support, and contain the following features:
- Customizable interface
- Change the default look and feel of the chat applets by modifying their text, fonts, colors, images, logos, advertisements, and sounds.
- Translatable interface
- Translate the chat applet interface into any language. Translations for Dutch, English, French, German, Italian, Portuguese, and Spanish are already provided.
- Color and font themes
- Define user-selectable color and font themes for the chat applets.
- Chat room advertisements
- Rotate banner advertisements in the chat rooms using static images or delivered dynamically by a local or remote ad server.
- Ability to ignore users
- Users can ignore others in the chat room so that all messages from another user are blocked from display.
Server Features
The VOLANO chat server is written in pure Java so that it can run on any Java-compatible operating system such as Microsoft Windows, GNU/Linux, Apple Mac OS X, Sun Solaris, or FreeBSD. Every VOLANO chat server has the following features:
- Unlimited chat users
- There is no artificial limit on the number of people who can connect to the chat server at the same time.
- Unlimited chat rooms
- There is no artificial limit on the number of chat rooms that can be made available in the chat server at the same time.
- Unlimited client Web sites
- There is no restriction on the number of different Web pages, Web sites, or domain names through which users can connect to a single VOLANO chat server using its built-in Java applets.
- Public chat rooms
- Public chat rooms are available in a list for all users to enter using the VolanoChat applet.
- Private chat rooms
- Private chat rooms are one-on-one chat sessions launched by double-clicking another user's name from within a chat room.
- Personal chat rooms
- Personal chat rooms are hidden from the list of public rooms and can be entered only through the Web page on which they're defined.
- Extensive access control
- Restrict access to users by their IP address or by the referring Web page through which they are connecting, or use external DNS-based blacklists to restrict access by IP address.
- External membership database
- Restrict access by member names and passwords in any external database or directory through the use of simple CGI scripts or servlets written in any language, such as PHP, Python, Perl, or Java. Restrict access to the chat server by members only, or allow both members and non-members to chat.
- Live moderated events
- Host live events with moderators and guests where the moderators receive questions from the audience and select among them to pose questions to the guest.
- Ability to remove, disconnect, or ban users
- Monitors can remove users from a chat room, disconnect users from the chat server, or disconnect and ban users from the chat server by IP address.
- Message flood control
- Slow down attempts to flood chat rooms with messages using the built-in and tunable flood control.
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- Customizable message logs
- Transcribe the chat message text of the chat rooms so that it's available for later use in either HTML or plain text.
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- Receive real-time reports on connection counts, memory usage, and message throughput from the chat server.
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- Write CGI scripts in any language that receive reports about events in a chat room, such as all chat messages and when users enter or leave the chat room.


The fields’ formal and spatial configuration exhibits a strong bond to the prevailing organization of space.
Rather than proposing new models of constructing or experiencing the built environment, as in the case of RTS,
Crimean occupiers are desperately trying to recreate present authorized forms of settlement. By adhering to a
strict grid-like, repeatable and uniform planning, they informally duplicate the typical act of subdivision used
in contemporary official housing development. In this aerial view [Figure 2], we can recognize the protest
field as an act of filling in, as a logical continuation of the surrounding authorized residential parcels.
This monotonous construction of housing emerges in stark contrast to a lost vernacular architectural diversity
that once prevailed in the territory. Prior to the annexation of Crimea by the Russian Empire in 1783, the Crimean
built environment exhibited great irregularity in planning and architecture, due to the variability of local
landscape and the influence of different Crimean tribes, such as the Tats, Nogais and Yaliboyu.23 Crimeans traditionally
built homes congruent with the landscape forms and irregularities. For example, the nineteenth-century traveler
Edward Clarke described the mountain Crimeans’ tendency to have their dwellings buried in foliage, while other
travelers mentioned their “habit of building their houses into the earth on the sides of terraced mountains.”24
The steppe regions of Crimea featured irregular streets and arrangement of housing.25 The coastal zones in the south
were distinguished by disorderly, multi-level structures, which included “crooked stone staircases in
narrow spaces between houses, which [were] piled one above the other; the courtyard [was] sometimes
located directly on the roof of the underlying house.”26
Russian rule significantly disrupted the peninsula’s existing architectural development, which is
most dramatically evident in Muslim religious architecture. Mass destruction of monuments and
mosques, as well as a discontinuance of state funding, resulted in a severe decline in the development
and typological diversity of local religious constructions.27 Tsarist repression of the peninsula’s Muslim
faith also resulted in mass (forced and willing) emigrations of Crimeans, for whom national identity was
tied strongly to religion rather than territory. In fact, a coherent Crimean identify was formulated
precisely in response to the foreign invasion, as the local tribes began to develop a sense of political,
cultural and linguistic unity. Moreover, the very concept of territorially-bound national identity was
imported from the Russian Empire:
Prior to the late 19th century, there had been no well-articulated political theory among the
Crimean Tatars which proclaimed their unique claim to, and identification with a particular
unit as a national homeland…Bernard Lewis writes, ‘The use of the word watan (Turkish
vatan) in a political sense, equivalent to the French patrie…dates from the late eighteenth
century, and is clearly due to European influence and example.’28
Younger generations of Tatars, educated and inspired by Russian intelligentsia and nationalists, promoted the
attachment of national identity to the physical landscape of the Crimean Peninsula. This territorial identification
was carried through and further consolidated during the Soviet rule, until the 1944 deportation, which marked a
decisive severing of a direct connection to the land.
The diversity that was once expressed through architecture became a sort of outside entity, which, upon return, began to
express itself in a uniform way. The typical execution of a protest field remains as follows: first, a group of Crimeans
is formed, usually from residents of the same village, living in multi-family houses. Then, as one ethnic Crimean describes:
They choose a neighbouring field and there they go, spontaneously – no one would touch a crowd. 100 people. They make
arrangements and negotiate and go, with measuring tapes, with ropes, pegs, axes, hammers – they reach the field and start:
so, here will be the street, and here will be plots of 6 or 8 acres. Measure and divide, measure and divide. They position the pegs,
tie the ropes. And immediately, so that everything would not be demolished, they start to deliver the stone and build the huts –
everything is done in a few days, a week maximum. Before the authorities come to their senses.29
What results is a rough but often consistent grid of huts. Depending on the situation, full houses are gradually built up in
place of the huts, roads are made, or the field can remain in this starting state for months or years. The constructed grid
of even lots and uniform dwellings appear as ghosts of a once rich diversity. Crimean occupiers’ informal re-enactment of
residential subdivision and their appropriation of urban planning vocabulary are pleas for acceptance and integration into
the working order. At the same time, through this massive collective effort, they are taking over the role of the official
property developer. Thus, their aims to include themselves into an established cultural fabric become acts capable of
disrupting or reconstituting social dynamics.
The recent annexation of the Crimean Peninsula from Ukraine by the Russian Federation in 2014, profoundly complicates
the protest field strategy’s social, political and symbolic significance. In the currently Russian-occupied territory,
the protest field becomes an increasingly loaded act, charged with multiple levels of tragedy and experience of displacement.
It is now not only a protest against a former abuse and an absence of reparation, but a simultaneous counter-occupation on a
micro scale, engaging with multiple layers of violence.
The Crimean Tatar strategy of claiming land through constructing protest fields in the Crimean territory is both a subversive
practice and a plea for acceptance into the established order. The protest huts function as monuments, placeholders for
completeness, performative acts of disruption and nonviolent tactics of claiming a home. By inscribing protest onto the local
landscape, Crimeans are attempting to create security on the levels of the body, the shelter, and the territory. The fields
operate simultaneously within the realms of the physical (material, bodily, structural), the symbolic and the social/political.
Displacement, placelessness, and precarity are well recognized conditions of the current moment. Histories of displacement, as
well as strategies to counter them form the everyday of our landscape. There is clear urgency for new and constantly evolving
strategies to reclaim agency within our built environment.


GSRS currently supports subunits smaller than 2000 residues.
For longer sequences, please upload a document to a reference, and apply it as a definitional reference.


The box model has elements on a web page takes up space, and the box model describes the space around the element.
Here's an example:
**WITHIN THE CSS FILE**
.my-class {
margin: 12 px;
border: 2px solid green;
padding: 4px;
background-color: orange;
}
**DISPLAYED IN WEBPAGE**
This is a box model example. See aren't I pretty?


Information Pioneers is a series I helped start in 2014 as part of SDSI, the Stanford Data Science Initiative. The goal was to bring thought leaders to campus to help students learn from visionaries in the data science field. It is currently hosted by the INFORMS chapter at Stanford.
Ion Stoica is the CEO of Databricks, and, at Berkeley, is a professor in CS and the former Director of the AMP Lab. Ion was interviewed by my colleague Britt Jamison. Errors and omissions are my own, and any good stuff can be completely credited to them.
Off the bat, let’s talk a bit about Databricks. Some folks might be familiar with Spark, but not Databricks - tell us more about that.
Databricks was founded in 2013. It is based around commercializing Spark, a project started in 2009 in Berkeley and open sourced in 2010. In terms of the product, Databricks is providing a hosted service which is based on Spark and the vision of the company is to simplify big data. Our aim is to build a platform in which you can run your data pipeline from data ingestion, preparation, interactive query processing, insights from machine learning, graphics, algorithms, all the way to data products.
You have been working around data for a long time. What initially got you interested in the field?
We started at Berkeley with these “Five Years Labs.” They try to serve big problems. Right now, we are doing AMP Lab - the previous lab was called RAM Lab, and was built around cluster computing. That was started around
- That got us in touch with a few companies, including Facebook. Their big data cluster was 80 nodes, and their team was 3 people - they hired a few folks from Yahoo and started doing new stuff, which was Hive on top of Hadoop. We started to work with them and then Matei Zaharia ended up creating Spark.
The first problem we tried to address with Facebook was getting around this problem of batch process and scheduling. Basically, Hadoop initially was a batch-processing system, with many jobs are submitted in a queue, and you process them in the order in which they arrive. This means, when you have a huge job of a few hours, the subsequent jobs, even if small, have to wait after a big job. We developed then an algorithm called “file sharing” which has become a popular scaling algorithm for Hadoop. It basically tries to create different queues and partitioned resources across jobs to process orders in tandem, large and small.
The other thing we worked on in 2006 was founding this company, Conviva. Conviva was peer-to-peer distribution. The application was video distribution. Quite soon, we switched from P2P to a model that uses existing distribution infrastructure, but we aggregated the intelligence and the decision-making. Still today, our customers are premium content providers. For example, we power HBO GO and MLB and places like this. We provide a service of real-time analytics: we take information from all of our clients and we aggregate them. We answer questions like, “Are there buffers? What is the bitrate we are streaming at?” The other side tries to provide hints to the clients about optimal bitrates and streaming for clients. That was the vision: real-time analytics and real-time control. Even then, it was big data. Right now, we have approximately 1 million clients at any given time. Then, Hadoop was only for historical data and batch computation. We started to build home-grown solutions, for streaming and for real-time control. We needed a distributed infrastructure for that. When I came back to Berkeley, I knew there were big problems - I had seen that.
The third thing was, in the RAD Lab, we were working next to the machine learning folks. And they wanted to do it at scale. Implementation at that time was very slow. Hadoop and MapReduce at that time were not very mature. Each ML algorithm is about iteration and converging to a great model. Each iteration on Hadoop is a MapReduce job. Between each job, the data gets written to disc on HDFS, and the next iteration needs to read it. This is very slow! We have this use case scenario: real-time streaming, real-time decisions, interactive query processing, iterative algorithms. When I got back to Berkeley, I had this seminar in 2009. These are one of the perks of the jobs. We had about 30 students discussing the problems in cloud computing and we would have class projects. One of the projects was Mesos. That was one of the first systems we started to build.
Mesos was a system that allowed multiple cluster computing frameworks to run on the same cluster. We were expanding the idea from the Facebook case (jobs of different size). There are really different categories of jobs and they can run on different cluster computing frameworks. For instance, MapReduce, or streaming systems like Storm, or MPI in high-performance computing. At that point, you have to create a cluster for everyone, but you don’t want that because it is very expensive. And how are you going to share data between the clusters? That is basically Mesos - trying to solve this problem for data centers. Writing multiple applications on one machine. Now, how do you illustrate and evaluate Mesos? You run MapReduce, and MPI, and many variations of MapReduce at the same time. This is pretty cool, but Mesos is something like an operating system. It provides a lot of services, provides a lot of plumbing. If a node fails, it thinks it is down, it tries to provide isolation between different slices of computing frameworks. We said, well - another way to evaluate this is to show it is much easier to develop a new class of computing framework on top of it because we an leverage all of the services of Mesos. There is no need for the services to do themselves, like scheduling. The first framework we developed was Spark. We said, ok, Hadoop-MapReduce is good at batch processing, so we are going to target some of the workloads we knew from our experiences in industry that are important. We focused on interactive query processing and iterative algorithms. Spark was created towards the end of 2009.
What a great history. It seems like a lot of tools have been developed since the 2002-2004 timeframe. What was the impetus for these tools to be developed? You have been fortunate to be in Silicon Valley - is this coming from a business need, or from the technical side? That is, there is so much data and processing to do that new tools need to be developed.
That is definitely the case, the latter. Many great ideas come from solving real problems. MapReduce comes from Google in 2004. It was a great piece of work. They had to process a huge amount of data, and needed a flexible system to do that. What were the choices at that time? One one hand, they could buy a big database: very expensive and inflexible. On the other hand, they could use some of the high performance computing software. That could be another. But that doesn’t really match the problem. They wanted to do ever growing amounts of data, cheaply. They had to solve the problem - they wanted to run the system, the software, on commodity hardware. The more nodes one has, the higher the probability of failure. There are software failures, and there are hardware failures. You need something fault tolerant. This is one of the places HPC fell short. They are supposed to run on supercomputers, which are very expensive. If there is a crash, they have to rerun the program. So, MapReduce - run on commodity software and be fault tolerant. The way it was fault tolerant: there are two stages, Map and Reduce, and each stage consists of a set of task and if one stage fails, the schedule has enough information about what has failed and it recovers that task by rerunning that task on the input data. The input data is typically replicated on three different nodes, so one can survive on two node failures or one direct failure. Very nice solution, general, flexible, and simple.
We talk a lot about MapReduce, which is maybe one of the most important papers written in the last 20 years. Now, some people are saying MapReduce is dead. Do you think so?
So, MapReduce is a logical decomposition of a parallel program. As a second item, MapReduce is an artifact, an implementation. I think it was a great first system. But in any area where there is a lot of work, there is a lot of progress. We learned a lot from MapReduce, and now there are second generation systems that are better. We generalize MapReduce and extend it, to get higher performance. One of these systems is Spark. It’s not about MapReduce about being dead, it is about a new generation that generalize and expands the framework.
So, do you see Spark as solving some of the problems with the MapReduce portion of Hadoop? Or do you see Spark as part of a larger system?
That’s a very good question. Right now, there are three main layers to Hadoop. First, the Hadoop Distributed File System, which is HDFS. Then there is YARN, which is similar to mesos mentioned earlier and supports different frameworks. And then on top of that, one framework is Hadoop MapReduce. Spark is an execution engine, a cluster computing framework, that is roughly at the same level as Hadoop MapReduce. On one side, Spark runs in the Hadoop ecosystem. If you have data in HDFS you can run Spark and process the data in HDFS. On the other side, because it is a general cluster computing framework, Spark doesn’t necessarily need HDFS and it can consume data from other distributed storage systems. “Running in the cloud” is on S3. Or from Cassandra. GlusterFS. One one hand it can be a part of a Hadoop stack, which occurs in many cases. There are also other contexts or deployments in which it is used.
I want to get into some of the problems Spark is solving. There is also a pretty big theme here of open source. It’s just exploded in the last 5 or 8 years. Being very involved in this community, what are your thoughts on why it has exploded? And what do you think the impact will be going forward?
Again, a very good question. The interesting thing here is that open source is exploding in multiple areas. For big data, it is one of the rare cases in which open source was the incumbent almost from Day 1. Google is very advanced, but everything is closed and they do not commercialize the systems because they consider it a key asset and competitive advantage. Microsoft has not moved that fast, despite being interesting, and has also not commercialized that work. We now have all of these startups beginning to get a lot of data - in particular Facebook and Twitter. And Yahoo creates Hadoop MapReduce. So everyone then takes Yahoo’s open source, Oracle is expensive, and there are no commercial offerings. It has limitations, though, so people begin innovating on top of that, since it is with Apache. That was the core with where we started. Now, all of the big offerings are open sourced.
There were quite a slew of companies in 2010-2012 that developed proprietary solutions to deal with Hadoop’s limitations. A new layer on top - cacheing for example. But there was so much energy going to open source, that it becomes difficult to remain competitive in the long run and keep your advantage. Many of these companies over time then shift to open source, for example, to Spark.
With regard to commercialization, I assume you are referring to companies like Cloudera, Hortonworks, MapR. Could you talk a bit about what that collaboration has been like?
Let me take a step back and talk a bit about our business, because I think it will help describe our relationship with Hadoop distributors. When we started with Databricks, we asked ourselves, “What kind of company do we want to build?” One obvious choice was to build something like Cloudera or Red Hat for Spark. We have our own distributions, and we support that with professional services and things like that. We did not do that. We decided instead to go for a service offering on the cloud. We saw it was the right time, and we saw it was in our DNA to go in that direction instead of a more support heavy company. Our strategy then became very simple: first, we want Spark to be successful as open sourced. The more successful Spark is, the more successful our services are. So, to promote this, we partner with the Hadoop distributors and encouraged them to provide some level of support to make it comfortable to include Spark in their distribution.
Could you explain a bit more about the cloud offering of Databricks? How do you see customers using your offering?
Fundamentally, “big data” work is pretty hard. What do you need to do this as a company? First, you have to put together a Hadoop cluster, which is not easy. Maybe you can get Cloudera or Hortonworks to help you with that. Then, you need to build a data pipeline on top of that. It is noisy, it needs to be cleaned, and turned into a table for interactive queries. So, at this point, Hadoop is no longer enough. Maybe you need another open source system: Presto from Facebook, Impala from Cloudera, or Drill from MapR. This is just some exploration to get some idea about the data.
Now you want more insights. To do that, you have to run some machine learning algorithms or graph algorithms. Maybe you want to productize these insights and develop a recommendation engine or something like that. One needs to deal and stitch together an extremely complicated system. This is where Databricks brings value. First, it uses Spark, which in itself is a unified engine. It supports batch processing, interactive query processing, streaming, machine learning, and graph processing. In addition, we also provide cluster management, which makes it very easy to spin off new clusters and scale them up and down, really in seconds. We then have a set of tools on top of that that make it much easier to interact with the data. We built a notebook for collaboration, there is interactive visualization, we can programmatically run jobs. The final piece is, “What are you going to do with the data?” Typically, you want reports or dashboards to share within the company. We make this very easy as well.
This is what you get: tools and a cluster manager, that is packaged with Spark, and sold as a service.
You mentioned how difficult it is unlocking value from data. That is probably a bit of an understatement, right? 60% of data projects - maybe even higher - fail. What are your reasons for this challenge? And how does Databricks help this?
The tools are still at the beginning. We are very early on. It is command line debugging, as opposed to being in the IDE. It’s pretty different, right? It is the difference between interactive debugging and debugging with screen tests in a program. The tools have a long way to go. The other reason for which there is no silver bullet… [there is an] assumption that, in the log, there is enough information to achieve the goal. This is sometimes not the case. You don’t have the data, or the data is too noisy. So the data itself is not enough to succeed. What can be done about this? Well, fail fast. Don’t spend 6 months and say, “Hey, there is not enough data.” You want to say that in two weeks. So you can go back and say, “We need more logs.”
So that’s what it boils down to: not enough data, inaccessible or clunky tools, and not enough trained people. Training is important. Data science today is a cool and hot job - but most people in industry don’t have formal training. When Databricks does training, we give out surveys. And 35% say they are data scientists and 40-50% say they are software engineers. When we ask them, where they are heading and where they want to go - now it is more like 50-55% of data scientists and 30% of software engineers. Many engineers want to be trained to become data scientists.
I like that you are speaking about skillsets in these organizations. We also are talking about Facebook and Google - do you think companies like Uber and Airbnb can also benefit from tools like Spark?
Well, they are definitely not small companies. And they can benefit - that is what is exciting about big data. It creates a lot of value. It is a fascinating thing. At the end of the data, what is Google, Facebook, or Twitter? They are data companies. That is where they are spending their engineering efforts. To build the tools, to build the systems, to mine the data, to build the data products. You can also see how this data is disrupting traditional industries. At the end of the day, Uber is building a pretty sophisticated infrastructure. Another example is Netflix. All of their infrastructure is in Amazon and it is very sophisticated, and celebrated. So, a lot of these companies are more nimble in using the data. It is pretty easy to get a lot of data. Successful applications will send a lot of logs back. The problem today is that many of these companies hope to get some value from the data, but it is hard. We see a lot of use cases. This goes from small startups to large companies.
Great, one last question from me. We’ve spent a lot of time describing the ecosystem today. What do you think the ecosystem is going to look like in five or ten years?
Yes, that’s interesting. In order to get an answer, one should look at what has happened in other similar cases. There are three phases. One phase is when a new problem creates a new technology to solve that problem. A second stage is the creation of new use cases from the new technology and people building additional systems for each of them. The third is then a consolidation. We have two examples for this. One, is mobile technology. Initially, it was the cell phone. There were new capabilities, you can call from anywhere. Then, at the end of the 90s, there was a 2nd stage. People want to take notes/PDAs in addition to calls, or maps, or play games. You get a plethora of devices to cover these use cases. This consolidates into a smartphone. And now there is a new wave: Waze, for crowdsourcing traffic, or Snapchat. They are possible because of this integration that took place in disparate devices over 15 years.
Big data initially was MapReduce - a new problem with new technology. Right now, we are pretty much in the second stage. You have a new workload? Build a new system. Machine Learning? Use Mahout. Graph processing? GraphLab or Giraph. Streaming? Storm. Interactive processing? Impala, or Drill. You want to put the system together, now, which is kind of hard. I think the next stage is going to be a consolidation. It may be Spark, or it may be something else. It will be a system or a platform that will allow you to run different workloads that create new applications. Let me give one example. Suppose you have data streaming in, and you want to have interactive queries on the streaming data. Something happens, and you want to drill down in seconds. Today, in the traditional Hadoop ecosystem, it is very hard to do. How are you going to have Impala operating on the system in Storm? You cannot. You need the move the data through HDFS to Impala, which already defeats the goal of interactive querying streamed data. Something like Spark can do this. The workloads share the same engine and data structures. It is already in place. I think this is what will happen.
Additional Q&A
- How do you view innovation coming out of academia vs. the commercial world? For example, Tachyon and most recently Velox out of AMP Lab.
- You discussed the importance of consolidating processing frameworks? What are your thoughts on using traditional storage, like Oracle systems, and have the processing done where the data is ingested?
- In terms of testing, how do you ensure that all the use cases are covered when testing is distributed?
- Knowing we have a bunch of folks thinking about their careers, what advice you would give for being able to stay at the “edge” of development?


At Abstract, we are passionate about all things digital.
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MUSIC_ST_PLAY 125 music.c if (!state_mtx_[current_state_][MUSIC_ST_PLAY]) return _FALSE; MUSIC_ST_PLAY 129 music.c current_state_ = MUSIC_ST_PLAY; MUSIC_ST_PLAY 223 music.c if (playFinalize_(MUSIC_ST_PLAY)) return;


"Breaking the Waves" (1996)
Review by Christopher Roberson
"Breaking the Waves" was one of the most highly praised films of 1996, on many different critics' top 10 lists. I had been unable to see the film in theaters, and was pleased when it became available on video. My eagerness to see it, however, rapidly turned to a sense of betrayal.  I have never been so disappointed after seeing a critics' favorite, and I think the widespread praise it has received needs to be answered with a dissenting opinion.
Earlier reviews typically provide the basic plot outline, so I'll skip that task. I do reveal some plot secrets, though. I appreciate that some readers may not yet have seen the film, so I have placed the plot-revealing paragraph at the end of the review, flagged in advance.
I'll move (roughly) from the film's best features to its worst. Clearly, its strongest suit is acting. Emily Watson deserved all the praise she received for her outstanding portrayal of Bess MacNeil, a slow-witted young Scottish woman who lives in an appallingly backward fishing village. Whatever else may be problematic about the film, at least this character stands out as fully-conceived and memorable. Also, a word of praise for Stellan Skarsgard, who plays Bess's husband Jan: he takes a severely underwritten role (his character has about ten lines total) and gives a sense of the character's presence and good humor.
I suspect that some critics were impressed by how well Watson took advantage of the histrionic opportunities provided by this role (which called for vomiting, screaming, eye-rolling, and stagey monologues where Bess talks to God, speaking for both herself and The Lord). But I think that Watson comes off well _despite_ the way her role is written: she took a scenery-chewing, meatball role and made the character convincing when it might have been just silly and overwrought. My only major reservation about the performance is her ripe, overdone faux-Scottish accent.
The production techniques also deserve some praise, for inventiveness at least. After being filmed on standard film stock, the image was transferred to video and then back to film. This produces a grainy, washed-out effect, devoid of bright colors or sharp outlines. Although the effect becomes wearisome, for a while it is striking. One is reminded of archival footage from some poverty-stricken Eastern European country, or the murkiness of the images in many of Fassbinder's films.
It seems likely that the director, Lars von Trier, was influenced by Fassbinder in more respects than his cinematography. His screenplay is like some of Fassbinder's, but unfortunately not in a good way. Von Trier seems to have seized on Fassbinder's use of melodrama while missing everything else that makes Fassbinder worth watching. Melodramatic elements are only worth retaining in a work of art if they are handled with sublety, or at least critical distance. Fassbinder was able to pull this off; von Trier doesn't even come close. Many reviews praised the film's "exploration of ideas" (such as the meaning and limits of love and the forms of religious devotion). But the "ideas" in _Breaking the Waves_ could probably be expressed in about three sentences. (Three brief sentences, using words of one syllable.)
Part of the problem is created by the strength and uniqueness of its central character, Bess. She is such an unusual character that many of the film's themes seem tied to her particular circumstances, and difficult to extend to normal people in any convincing way. But the main problem is that most of the characterization is thin and unconvincing. The film is dedicated to telling us how deeply Bess loves Jan, and to what depths she is willing to sink in her devotion to him. It tells us these things, but it never tells us _why_. Bess loves Jan more deeply than anything else, and presumably that's because he is special in some way; but as far as the audience is concerned, Jan is two-dimensional, hard to distinguish from any of the other young male characters. What attracted her to him initially? Why did she fall so deeply in love with him? We never really know. Von Trier doesn't bother to develop Jan's character or the relationship between Jan and Bess. He seems to have thought that long, explicit sex scenes were sufficient; but they're not.
The superficiality of the script becomes more evident as the film proceeds. Astonishingly, some critics seemed to think that von Trier was making a brave statement about sex and love: but his statements are hackneyed, and he aims at easy targets. For instance, he boldly suggests that the church elders of Bess's village, all dour, women-hating Scottish Calvinists, might be wrong about sexual morality and what love really means. When (since Hawthorne, anyway) has this been a shocking or original idea? Who in the audience is likely to side with the church elders? Who would side with them even if von Trier had tried to portray them as anything other than dogmatic, cruel, and hateful? For a film that makes a point of "savaging the drab conformity of community life," there is absolutely no sense of community among these people -- after the first ten minutes, I never had a sense that the villagers related to each other in any way that wasn't directly intended to advance the plot, usually by showing how repressed and judgmental they were.
Even worse than the writing, though, is the cinematography. The director chose to use only hand-held cameras. His aim seems to have been a "cinema-verite feel," but any hint of cinematic realism is constantly undercut by the framing, which includes numerous close-ups and unconvincing camera angles which would never occur in actual cinema verite. Far from being realistic, the camera is constantly calling attention to itself, diverting attention from the actors. One constantly wonders whose perspective the camera work is supposed to capture. Certainly not any of the film's characters. Not the camera operator, either, since this isn't _real_ cinema verite, and the actors never react as if there's a camera present. I'll also rule out the audience, unless the director was trying to reach an audience of people who suffer from neck spasms and tremor. Even scenes without any movement or action never stand still, not for a second.
And then there is the worst aspect of _Breaking the Waves_: the editing. Von Trier's confused notions of cinematic "realism" led him to include gratuitous cuts in the middle of static sequences, for no apparent reason, while eliminating normal shift-of-perspective cuts in other scenes. In the latter scenes, the hand-held camera follows the action with quick, vertiginous pans. The effect, sadly, is not so much realism as nausea. And even this pales when one considers the editing of most of the individual scenes: slack, bombastic, intentionally "arty." In almost every scene, Von Trier seems to have gone out of his way to include tedious extra footage that no one could possibly care about. It is this, above all, that makes _Breaking the Waves_ not just an overwrought melodrama, but a 160-minute-long overwrought melodrama.
I should probably repeat that this film is _two hours and forty minutes long_, and it feels longer. Much longer. Much, much longer. It could easily have been forty to sixty minutes shorter, simply by cutting out extraneous scenes and editing the remaining ones less slackly. One particularly grating element is the inclusion of long, unnecessary "chapter title" sequences (complete with snippets of mediocre early-70s pop songs), which serve both to break up the narrative flow and to drag things out even more.
If you are still interested in seeing the film, you may want to stop reading at this point.
One question that doesn't really fit anywhere else: what the hell does the title mean?
Although the plot is never particularly convincing, it completely unravels in the last twenty minutes. It's bad enough that Jan recovers miraculously from his paralysis after Bess martyrs herself. By golly, she wasn't schizophrenic, she was Really Talking To God. (Perhaps this was one of the "original, challenging ideas" critics mentioned?) But the film tops itself with a jaw-droppingly literal, saccharine conclusion. Early in the film, we are told that Bess's church has no bells; but Bess remarks to a friend that she likes church bells. The final scene, believe it or not, is of bells ringing miraculously in the open sky above Jan's oil rig. We know that the bells are miraculous because we're told that they don't show up on the rig's radar. Yes, that's right, folks: God was on Bess's side the whole time, and now we have proof.
What's particularly depressing is not so much that the film is bad. What's depressing is that it was greeted with rave reviews. This leads me to two pessimistic conclusions. One is that critics are far too willing to overlook artistic bombast if they're captivated by a good performance, or if the film has one or two more ideas than is typical. The other is that von Trier can only have been encouraged by the critical reaction, and is likely to produce something even worse next time. At least when his next film comes out, I'll know in advance that I should avoid it.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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Related links: index of all rec.arts.movies.reviews reviews


Living Room
- Hardwood flooring
- Dual privacy blinds
- Vaulted entry
- Recessed lights
Light-filled two-story townhouse in the Prairie Manor subdivision located right next to the Prairie Path and Pioneer Park near Vallette & York. Come see why this area is the best-kept secret in Elmhurst! Enjoy convenient living with no exterior maintenance and the modern amenities you won’t find in rehabbed homes from the 50s and 60s.
| Property Type | 2-story Townhome |
| Parking | 2 car attached garage |
| Basement | Full, unfinished |
| Year Built | 1992 |
| Lot Size | 1,742 ft2 |
| Attic | Yes |
| Exterior | Brick |
| Roof | Shingle |
| Driveway | Pavers |
| Status | Active |
| Taxes | $6,973 (2012) |
| HOA Fees | $285 per month |
| Ownership | |
| Last Sold |
| Schools | Elmhurst school district 205 |
| WalkScore | 57/100 (somewhat walkable) |
| Heating | Gas, Forced Air |
| Cooling | Central A/C |
| Utilities | Public Sewer & Water |
| Lincoln Elementary School | 0.5 mi |
| Sandburg Middle School | 1.2 mi |
| York Community High School | 1.1 mi |
I've lived here for the past 3 years and love it! Get in touch and I'll be happy to show you my home.


A C-based API for monitoring and managing various states of the NVIDIA GPU devices. NVIDIA Management Library (NVML) provides a direct access to the queries and commands exposed via nvidia-smi. NVML is included with the NVIDIA Linux graphics driver.
To write applications against this library see the NVML developer page: http://developer.nvidia.com/nvidia-management-library-NVML
To include NVML functionality in scripting languages see: http://search.cpan.org/~nvbinding/nvidia-ml-pl/lib/nvidia/ml.pm and http://pypi.python.org/pypi/nvidia-ml-py/


Mobile Developer
Web Developer
Where the idea comes from?
Visually impaired people can surf on the internet easily by reading text
But internet not only contains text, it is also a huge image library
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Think think think!
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Betim is rising
Betim is a image based recognition web tool
It has two main sources (image recognition api(growing) & helper community)
Image from web site asked them
And visually impaired person knows the content of the image he/she select
What is the meaning of all?
Betim will become also huge image contents with meaning
Computers can analyse these data by using human factor
Image recognition algorithm will be more accurate by using this source
Not only for visually impaired people, but also for all humanity
http://github.com/betim-app
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Grading DApp
Admin
Professor
TA
Student
List of addresses of Instructors
Add
Get the List of Addresses of Instructors
Get
Instructors List
Course ID
Course name
Roll list of enrolled students
List of addresses of Students
List of addresses of TAs
Add
Course ID
Exam type
Total marks
Roll list of students
Marks list
Add
Course ID
List of Weightages
Grade cut-off
Calculate
Course ID
Exam type
Roll list of students
Marks list
Update
Course ID
Exam type
Get
Score Sheet for the exam
Course ID
Get
Exam Details
Course ID
Get
Student Grades Sheet
Course ID
Roll Number
Get
Students Report
Admin Function Panel
addInstructor
getInstructorsList
Prof Function Panel
addCourse
addExam
calculateGrades
updateMarks
getProfExamMarks
getProfExamWeightages
getProfMarksGrades
TA Function Panel
addExam
updateMarks
Student Function Panel
getStudentMarksGrades


Provides user-friendly tools for calibration in survey sampling. The package is production-oriented, and its interface is inspired by the famous popular macro 'Calmar' for SAS, so that 'Calmar' users can quickly get used to 'icarus'. In addition to calibration (with linear, raking and logit methods), 'icarus' features functions for calibration on tight bounds and penalized calibration.
| Version: | 0.3.0 |
| Depends: | R (≥ 3.1.1) |
| Suggests: | testthat, ggplot2, Rglpk, slam, xtable |
| Published: | 2017-03-04 |
| Author: | Antoine Rebecq [aut, cre] |
| Maintainer: | Antoine Rebecq <antoine.rebecq at m4x.org> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Citation: | icarus citation info |
| Materials: | README |
| In views: | OfficialStatistics |
| CRAN checks: | icarus results |
| Reference manual: | icarus.pdf |
| Package source: | icarus_0.3.0.tar.gz |
| Windows binaries: | r-devel: icarus_0.3.0.zip, r-release: icarus_0.3.0.zip, r-oldrel: icarus_0.3.0.zip |
| OS X binaries: | r-release: icarus_0.3.0.tgz, r-oldrel: icarus_0.3.0.tgz |
| Old sources: | icarus archive |
Please use the canonical form https://CRAN.R-project.org/package=icarus to link to this page.


--- layout: post title: "Still, stuck on thinking a good brand name for a business?" subtitle: "" date: 2020-01-28 23:45:13 -0400 background: 'https://i.ibb.co/Ry6v8Cd/pexels-negative-space-34639.jpg' category: Tech ---
I was sitting on my couch, as usual working on my laptop; with my so-called work from home internship. I was really bored and with sleepy and suddenly a primary school girl kid from the neighbourhood came to my house. She saw me working on my laptop. She was staring at the rear side of my laptop and started reading:
I literally went blank for a while I just wondered what exactly did she want to know about. As I said, she was just a small kid; Mr Hewlett Packard would never have thought to keep the brand name of his name initials which could be questioned like this but he was never wrong. Nowadays initials and short forms are being used more by businesses in thier brands name.
So, the moral of the story is that if a brand really wants to stand apart they should come with unique names that should be more relevant to the brand's propaganda of just using initials and short adjectives won't work because till the date there are numerous business using only initials in their brand name.
Here are some major key points to note before you start thinking for a great brand name:
• Whatever brand name you're choosing it should be relevant to your product or business and it should be descriptive.
• Avoid using initials because till 2019 many brands have been established with their name only consisting initials.
• prefix suffix, compound words, verbs and trending slangs(if they are relevant to your product) words with a change in spelling but displaying it with creative graphics.
• At last, also understand you're customer base their age group and targeted audience especially if your business like is news media or Entertainment blog you might be dependent on social media platforms so the name should be easily recalled by the audience example: Speaking tree
Photographs by Unsplash.


|
Marian
v1.6.0
A C++ GPU-specific Neural Machine Translation
|
#include <node.h>
Public Member Functions | |
| Node (Ptr< ExpressionGraph > graph, Shape shape, Type value_type=Type::float32) | |
| virtual float | scalar () override |
| virtual NodeOps | forwardOps () override |
| virtual NodeOps | backwardOps () override |
| virtual void | runForward (const NodeOps &ops) |
| virtual void | runBackward (const NodeOps &ops) |
| virtual void | forward () override |
| virtual void | backward () override |
| virtual bool | trainable () override |
| virtual void | setTrainable (bool trainable) override |
| virtual bool | memoize () override |
| virtual void | setMemoize (bool memoize) override |
| virtual void | setId (size_t id) override |
| virtual size_t | getId () override |
| virtual void | increaseEdges (size_t edges=1) |
| virtual void | decreaseEdges (size_t edges=1) |
| virtual size_t | edges () |
| virtual Ptr< ExpressionGraph > | graph () override |
| virtual void | debug (const std::string &message) override |
| virtual bool | marked_for_debug () override |
| virtual const std::string & | debug_message () override |
| virtual size_t | allocate () override |
| virtual void | free () override |
| virtual void | init () override |
| virtual void | init_dependent () override |
| virtual void | set_zero_adjoint () override |
| virtual Tensor & | val () override |
| virtual Tensor & | grad () override |
| virtual const Shape & | shape () override |
| virtual const Type & | value_type () override |
| void | set_name (const std::string &name) override |
| const std::string & | name () const override |
| virtual const std::string | form () override |
| virtual const std::string | color () override |
| virtual const std::string | label () override |
| virtual std::string | graphviz () override |
| virtual std::vector< Expr > & | children () override |
| virtual Expr | child (size_t i) override |
| Ptr< Backend > | getBackend () |
| void | record (Ptr< AutoTunerRecorder >, size_t, bool) override |
| Public Member Functions inherited from marian::Chainable< Tensor > | |
| virtual const std::string | type ()=0 |
| virtual size_t | hash ()=0 |
| virtual bool | equal (Expr)=0 |
Protected Attributes | |
| size_t | id_ {0} |
| size_t | edges_ {0} |
| bool | trainable_ {true} |
| bool | destroy_ {true} |
| bool | memoize_ {false} |
| std::vector< Expr > | children_ |
| Weak< ExpressionGraph > | graph_ |
| Shape | shape_ {1, 1, 1, 1} |
| Type | value_type_ {Type::float32} |
| std::string | name_ {"none"} |
| Tensor | val_ {nullptr} |
| Tensor | adj_ {nullptr} |
| bool | markedForDebug_ {false} |
| std::string | debugMessage_ |
| Ptr< AutoTunerRecorder > | recorder_ |
| size_t | recorderHash_ |
| bool | recorderStop_ |
Main node class for computation graph, implements most common functions demanded by Chainable. Each operation in a computation graph is a node.
|
overridevirtual |
Initialization for backward step of top node in computation graph. Allocates memory and sets gradient to 1 (df/df == 1).
Reimplemented from marian::Chainable< Tensor >.
Reimplemented in marian::StepNodeOp, and marian::ReshapeNodeOp.
|
overridevirtual |
Initialization for backward step of any non-top node in computation graph. Allocates memory and sets gradient to 0 for further accumulation of gradients from all parents.
Reimplemented from marian::Chainable< Tensor >.
Reimplemented in marian::StepNodeOp, and marian::ReshapeNodeOp.


This is a navigation using js
My profile
about me
career
Interests
technical interest
personal interests
Connect
facebook
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linedin


I play for my high school basketball team, Fannie Lou Hamer Freedom High School. I am 17 and my goal is to change the world through basketball. I am a big anime fan, I like video games such as RPG and shooting games. any type of shooting based game.
I was the leading scorer for my basketball team in middle school averaging 20pts 8reb 4ast, also i was a two time MVP my 7th and 8th grade year.
I normally write poems in my spare time but i don't show anyone. I think it's fantastic though.
I play varsity basketball in the B division for my high school and I already have 2 scholarships and they want me to join thier summer camp next summer.
I feel being a video game tester is anacomplishment because playing games is one of my favorite things to do and now I can get paid for it. By far the best job I've had so far.
Hey as you may know my name is Latrell Anderson and I love the game of basketball and I plan to go pro with basketball. I would love for you to contact me sometime to talk about anything you want. I want to become world wide and help those in need through basketball, That is my goal in life.


{%extends "blog/base.html" %} {% block content %} {% load staticfiles %}
Once you have logged in the main page will as shown above.All featues of the Librolet will available for use.User can view details of other posts and related data.


AWS Config ConfigurationRecorder RecordingGroup
RecordingGroup
is property of the AWS::Config::ConfigurationRecorder resource that defines which AWS
resource types to include in a recording group.
Syntax
JSON
{ "AllSupported" :
Boolean
, "IncludeGlobalResourceTypes" :Boolean
, "ResourceTypes" : [String, ...
] }
YAML
AllSupported:
Boolean
IncludeGlobalResourceTypes:Boolean
ResourceTypes: -String
Properties
AllSupported
-
Indicates whether to record all supported resource types. If you specify this property, do not specify the
ResourceTypes
property.Required: No
Type: Boolean
IncludeGlobalResourceTypes
-
Indicates whether AWS Config records all supported global resource types. When AWS Config supports new global resource types, AWS Config will automatically start recording them if you enable this property.
Note
If you set this property to
true
, you must set theAllSupported
property totrue
.Required: No
Type: Boolean
ResourceTypes
-
A list of valid AWS resource types to include in this recording group, such as
AWS::EC2::Instance
orAWS::CloudTrail::Trail
. If you specify this property, do not specify theAllSupported
property. For a list of supported resource types, see Supported resource types in the AWS Config Developer Guide.Required: No
Type: List of String values


1,426
Total services
63,262
Total Instances
24
Not Up Instances
6,718
Memory Danger Instances
微服务总览
ServiceName
ServiceAddress
ServicePort
Status
Mem
Metadata
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