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CES 2020Audio Video NewsNew Products
Al Griffin | Jan 17, 2020 | 0 comments
CES over the past few years has become more about vision than sound, with statement TVs in booths lining the Las Vegas Convention Center's Central Hall vying aggressively for the attention of show attendees. CES 2020 was no different, and in some ways was even more TV-centric due to the wide range of new products and prototypes on view from both established brands like LG, Samsung, and Sony, and relative newcomers such as Hisense, Konka, and TCL. Yes, LG once again had motorized rolling OLED TVs at the show — a product promised for 2019 that never materialized here — along with new OLED prototypes that roll down from the ceiling. But what most interested me was the sets that are likely to arrive in 2020, especially the innovative ones that will actually be affordable.
Audio Video NewsNew Products
SV Staff | Jan 16, 2020 | 0 comments
In its latest attempt to get rid of messy wires, Canadian connectivity specialist Nyrius today introduced a $300 wireless transmission system it says can stream 1080p high-definition video and audio up to 165 feet away with zero latency.
| Jan 16, 2020 | 0 comments
KEF today announced the addition of a versatile, high-performance THX-certified in-wall speaker and new finish options for its Reference Series line of speakers whose origins date back to 1973.
Amid the hustle and bustle of CES 2020, China-based headphone specialist 1More announced that it's getting ready to ship a new $200 set of wireless, active noise canceling (ANC) earphones at the end of the month.
JVC is revisiting the high-end headphone category in the U.S. with two upscale in-ear models, one featuring a stainless-steel housing, the other a unique wood-cone driver and housing.
| Dec 27, 2019 | 0 comments
As a follow-up to the FS-HAL1 earphones that shipped earlier this month, Atlantic Technology is now shipping its first full-size wireless headphones, previewed almost a year ago at 2019 CES.
Amina Perfects the 'Invisible' Speaker
SV Staff | Dec 26, 2019 | 0 comments
Amina Technologies, the U.K.-based maker of "invisible architectural speakers," say it has updated the performance and installation flexibility of its sound panels, which use exciters integrated in aluminium honeycomb or composite soundboards to radiate sound.
Mixxim Brings Back the Cassette… As a Music Player
The audio cassette just won't die. The latest attempt at reviving the '70s relic comes in the form of a Bluetooth-enabled digital music player that doubles as a legitimate cassette — legit as in it will actually play in some cassette decks.
T+A Announces 3rd Generation of All-in-One Audiophile Music System
The German audio company T+A (Theory + Application) has announced the third generation of its all-in-one Caruso Music System built for audiophiles.
McIntosh Mates Tube and Solid-State Amps in Bold Dual-Mono Design
McIntosh today introduced a one-of-a-kind dual-mono amplifier that combines a 300-watt tube amplifier, 600-watt solid-state amplifier, and crossover in one chassis designed to provide the "ultimate" bi-amping solution.
Denon Previews New Heos/AirPlay 2-Compatible Wireless Speakers
Denon today previewed a new series of compact speakers that incorporates its high-resolution-capable Heos wireless multiroom platform and supports streaming via Apple's AirPlay 2 and Bluetooth.
SVS 'Reimagines' its Most Popular Subwoofers
Subwoofer/speaker specialist SVS today introduced a new series of sub-$1,000 subwoofers to replace its popular 2000 Series models, including the Top Pick-designated SB-2000 and PB-2000 models.
Atlantic Technology Rolls Out its First Earphones
Atlantic Technology today announced the availability of its first earphones.
Rotel Reintroduces Upscale Michi Brand in U.S.
Rotel today announced plans to reintroduce the high-end Michi brand in the U.S. early next year.
Denon's $249 Soundbar Boasts DTS Virtual:X Surround
Denon today introduced a $249 all-in-one soundbar featuring DTS Virtual:X processing, which simulates a DTS:X-like immersive sound field without using up-firing height speakers.


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Hardcover, 9 x 11.25 in. / 210 pgs / 100 color / 40 bw.
Pub Date 10/27/2015
Edited by Michael Juul Holm, Tine Colstrup. Introduction by Tine Colstrup, Poul Erik Tøjner. Text by Rainer Stamm, Uwe Schneede, Wolfgang Werner, Diane Radycki, Rineke Dijkstra.
Considered one of the most important representatives of early German Expressionism, painter Paula Modersohn-Becker (1876–1907) left behind an oeuvre of still-life paintings, landscape paintings, portraits and self-portraits in a characteristic expressionist style upon her premature death at the age of 41. Famous for her friendship with poet Rainer Maria Rilke and for having influenced Picasso, Modersohn-Becker is also recognized as a pioneer for female artistic representation of the female body, and is often cited as the first woman to paint full-length nude self-portraits. This volume, published on the occasion of Louisiana's retrospective exhibition, includes essays by experts focusing on different aspects of the artist and her oeuvre, as well as contributions from contemporary artists Rineke Dijkstra, Chantal Joffe and Daniel Richter. With more than 100 images, the book is an accessible introduction to this inaugural Expressionist about whom little has been published outside of Germany.
PAULA MODERSOHN-BECKER MONOGRAPHS + ARTIST'S BOOKS
PAULA MODERSOHN-BECKER: SELF-PORTRAIT WITH TWO FLOWERS
Text by Diane Radycki.
FORMAT: Hbk, 9 x 11.25 in. / 210 pgs / 100 color / 40 b&w.
PUBLISHER: Louisiana Museum of Modern Art
AVAILABLE: 10/27/2015
Published by Louisiana Museum of Modern Art.

redpajama_set_name: RedPajamaCommonCrawl


Rancilio Specialty makes its grand debut in China with the RS1 at Shanghai's Hotelex, in a stand that will also offer an overview of Rancilio coffee machines and two different Extraction Lab stations. Taking place from April 1st to the 4th, the international event — one of the largest and most important in the East with 26 years of experience showcasing the leading equipment in both hospitality and fine food — will unfurl within the Shanghai New International Expo Center. Here, over 220,000 m² will host 2,500 exhibitors, welcoming nearly 150,000 visitors and more than 5,000 overseas buyers, where the new Rancilio Specialty brand will break onto the Chinese market for the first time ever.
For the occasion, Rancilio Specialty will present two 3-group RS1 coffee machines personalized in two limited editions - White & Wood and Black & Wood - alongside the standard 2-group stainless steel version.
Visitors will be able to discover the signature patented Temperature Profiling technology, the touchscreen interface, and the variable steam lever.
At the other end of the stand, Rancilio Group will also present the entire gamut of Rancilio products: Rancilio Classe 5, Classe 7, Classe 9 and Classe 11 in 2 or 3 group versions equipped with various configurations designed specifically for the Chinese market trends. Each coffee machine will be presented in the Tall version, offering optimal conditions for extracting coffee in large takeaway cups, which are popular throughout all of Asia.


text:
module TDev.RT {
    //? Doc rendering helpers
    export module Docs
    {
        //? Renders a bitmatrix
        //@ docsOnly [bits].lang("bitmatrix")
        export function bitmatrix(bits: string) {
            // used when rendering docs
        }
    }
}

redpajama_set_name: RedPajamaGithub


text:
Acropora indonesia е вид корал от семейство Acroporidae. Видът е световно застрашен, със статут Уязвим.

Разпространение
Разпространен е в Австралия, Индонезия, Малайзия, Мианмар, Папуа Нова Гвинея, Сингапур, Соломонови острови, Тайланд и Филипини.

Описание
Популацията им е намаляваща.

Източници

Acropora

redpajama_set_name: RedPajamaWikipedia


Download the latest drivers for your HP Scanjet to keep your Computer OS: Windows 10 32 & 64bit, Windows 32 & 64bit, Windows 7 32 & 64bit. 2 Sep HP Scanjet With the HP Scanjet digital flatbed scanner you will enjoy photo-quality results and crisp text at dpi optical resolution and bit. 98//XP. Additional Requirements, Windows 98/ME//XP. This package supports the following driver models:HP Scanjet bit SP 1; Windows Vista AMD bit; Windows XP Itanium bit; Windows NT 4 SP 4.
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Official Hewlett-Packard HP Scanjet Drivers download center, download and update Hewlett-Packard HP Scanjet drivers in 3 steps Windows 7 64 bit. Download the latest and official version of drivers for HP Scanjet Scanner series. This driver package is available for 32 and 64 bit PCs. Hp Scanjet Driver for Windows 7 32 bit, Windows 7 64 bit, Windows 10, 8, XP. Uploaded on 4/10/, downloaded times, receiving a 92/ rating.
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Jul 10, · Can I use scanjet with windows 7. Scangear for 64 bit Windows 7; Scanjet driver for Windows 7; Hp scanjet c windows 7 driver; HP ScanJet C.


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Traders point christian school
Newbuildings then and now
January 21, 2009 londonderrysentinel Leave a Comment
There are few villages in Ireland which can boast the same cultural heritage and diversity as Newbuildings. Enjoying the benefits of a tidal river, it has been a centre of human activity since the Third or Fourth Century. Historically, Newbuildings has had its own Castle and Abbey, as well as being an area populated during The Plantation of Ulster, and a centre for employment due to its water mill. In the first of a series of features on the village, Sentinel reporter Olga Bradshaw delves into the history of Newbuildings and is told some very 'fishy' tales. A truly historic village with colourful charactersTHE next time you find yourself in Newbuildings waiting for someone in the car park to the rear of Villa's pub look across the road to the row of houses. Imagine a time before the houses existed; to the left (where the trees are now) used to be the dam wall and directly between the car park and the houses in the distance, was the dam itself, into which a burn flowed from the right. … [Read more...] about Newbuildings then and now
News mot newbuildings, newbuild 808 state, p&o ferries newbuild, foyle vets newbuildings, vale beefs up fleet renewal to 30 vloc newbuildings
What's On Listings 13-19 June
June 13, 2019 clitheroeadvertiser Leave a Comment
To announce an event or gig, call 01772 838196 or email [email protected] TheatreChorley CADOS Present - A Bunch of Amateurs: comedy written by Ian Hislop and Nick Newman. From £8. 7.30pm. Chorley Little Theatre, Dole Lane, Chorley. Tel: 01257 264362. Opens, Mon 17 Jun until Sat, 22 JunDarwen Mary, Queen of Scots (15): Saoirse Ronan as Mary, Queen of Scots and Margot Robbie as her cousin Queen Elizabeth I. £5. 2pm. Darwen Library Theatre, Knott Street, Darwen. Tel: 0844 847 1664. Wed, Jun 19Lancaster Eyebrow Guy: Gary Meikle the social media ranter on eyebrows and hair extensions. £12. Lancaster Comedy Club, The Borough, Dalton Square, Lancaster LA1 1PP. Tel: 01524 64170. Sun, Jun 16 onlyLytham Cranford: Chapterhouse Theatre open Lytham Hall's outdoor season with Laura Turner's adaptation of Elizabeth Gaskell's most acclaimed novel. £14, child £7. 6pm. Lytham Hall, Ballam Road, Lytham. Tel: 01253 736652. Sun, Jun 16 onlyMusicChoral Exultate … [Read more...] about What's On Listings 13-19 June
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Meet the Reigate & Banstead Local Election candidates
April 23, 2019 getsurrey Leave a Comment
New ward boundaries mean that every seat in every ward is up for election on Reigate and Banstead Borough Council (RBBC) in May. Currently, the Conservatives hold a commanding majority with 39 of the council's 50 seats. Residents' Associations in Nork and Tattenhams are second with a combined six councillors. Each ward will be electing three councillors, meaning that residents will be required to vote for three candidates. The new makeup of RBBC will consist of 45 councillors. Polls open at 7am on May 2 and close at 10pm, with the votes counted the next day. In addition, seats on Horley Town Council and on Salfords and Sidlow Parish Council will be contested so residents in those areas will have more choices to make. Just for fun... These were the most commonly used words in the Reigate and Banstead election bios (story continued below). Below are all the candidates who are standing for election in each ward. SurreyLive gave them 100 words (they've been cut off if they've gone … [Read more...] about Meet the Reigate & Banstead Local Election candidates
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Paths from the past: historians make sense of Brexit and our current political turmoil
March 30, 2019 theguardian Leave a Comment
"Fromage not Farage", "I've Seen Better Cabinets at Ikea": the signs at the Put It to the People march were particularly British in their humour. The present crisis in the United Kingdom is, however, no laughing matter. It reflects very real social and political problems and deep divisions about the future of the country. The argument over remain or leave is also between the winners and losers in globalisation. In the 1960s former US secretary of state Dean Acheson said Britain had lost an empire and was yet to find a role. For a time it appeared Britain could be the pivot of Europe and the Atlantic world. Britain would be part of Europe on its own terms but also have a special relationship with the United States, with the British acting, as Harold Macmillan said, as the Greeks to the Americans' Romans. Membership of the European Union has disappointed many and the special relationship is today as flimsy as the friendship between Donald Trump and … [Read more...] about Paths from the past: historians make sense of Brexit and our current political turmoil
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Moomins want to raise a million euros to protect the Baltic Sea
Flemish Speaking Customer Service Agents
Swedish/Danish/Finnish Customer Service Specialist
Danish Speaking Customer Service Agent
Real Betis willing to spend up to 30 million euros for Fekir
'We have to respect their laws'
Rain not spoiling the party as the Open returns to Portrush
'I would like to punch myself'
Taking the plunge has a depth of benefits
Man sleeping on the streets found dead in Cork city centre


9x12, xvii + 177 pp.
This volume contains trio sonatas written during the early part of Telemann's career, when he was heavily influenced by the sonatas of Corelli and the dance music of Jean-Baptiste Lully. Probably composed before 1715, these works reveal an unfamiliar side of the composer, once far removed from the galant style and "mixed taste" of the music he published during the 1720s and 1730s. The trio sonatas encompass a variety of styles and movement types, including French preludes and dances, ostinato-based slow movements, vigorous fugues, some of the earliest known sonata movements in ritornello form, and a late example of the stylus phantasticus in instrumental ensemble music. Seven of the works are scored for two violins (including two trios that call for scordatura); three are suitable for pairs of flutes, oboes, or violins; and the remaining two are for oboe and violin, and flute and viola da gamba.


BarkGrowlBite: SUSPECTED MURDERER ARRESTED CROSSING INTO U. S.
SUSPECTED MURDERER ARRESTED CROSSING INTO U. S.
Juan Antonio Prado was arrested on Saturday on a murder warrant from a 2016 homicide in San Joaquin County. He had fled to Mexico after the killing.
The newspaper article I read on this did not specify whether or not Prado was a U. S. citizen or legal resident alien.
EDITOR'S NOTE: I read several reports on this arrest and none of them identified Prado as an illegal alien.


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InfoQ Homepage Presentations Michelangelo Palette: A Feature Engineering Platform at Uber
Michelangelo Palette: A Feature Engineering Platform at Uber
View Presentation
Amit Nene and Eric Chen discuss the infrastructure built by Uber for Michelangelo ML Platform that enables a general approach to Feature Engineering across diverse data systems.
Amit Nene has led engineering teams on the Michelangelo ML Platform and Risk Platform at Uber, driving several projects such as the Palette feature store, feature pipelines and data engineering, and feature transformers. Eric Chen leads the offline model processing pipelines and model online and offline serving accuracy of Michelangelo ML Platform at Uber.
QCon.ai is a practical AI and machine learning conference bringing together software teams working on all aspects of AI and machine learning.
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Presented by: Heidi Waterhouse - Principal Developer Advocate
Nene: Let me start by quickly introducing ourselves. My name is Amit Nene, here with me is Eric Chen, we work on Uber's Michelangelo machine learning platform. I've been working on the platform for a little less than a year, Eric has been a veteran. We're going to talk today about Michelangelo Palette, which is a system within the greater Michelangelo platform that enables feature engineering for various use cases at Uber. Feature engineering is the use of domain knowledge to prepare features or data attributes for using machine learning.
Michelangelo at Uber
Before that, I want to give a small overview of the Michelangelo platform and what it does. Michelangelo platform is a proprietary platform that was built at Uber and our mission there is to enable engineers and data scientists across the company to easily build and deploy machine learning solutions at scale. Essentially, you can think of it as ML as a service within Uber and we give you all the building blocks for creating your end-to-end machine learning workflows. For instance, the first part is about managing your data, mostly features, we give you the tools, the APIs, to manage those. The second part is creating your workflows, API's to create end to end workflows for your supervised, unsupervised deep learning models. We support a variety of different tools because machine learning at Uber uses a wide variety of libraries and tools across Uber, so we want to make sure our workflows are built in a heterogeneous way.
Having trained your models, we give you the ability to deploy them in production, in various different environments, like batch environment for offline predictions, real time online predictions, and mobile serving environments. More recently, we've been working on tools to detect drips in features or your data, and also your models, the model performance, both offline tools as well as online tools.
That was a quick overview of what Michelangelo is at Uber. It's not an open source platform, but the purpose of this talk was to really share ideas around how we approach feature engineering, which I'll get to in a bit and also give you guys an overview of the architecture of the subsystem involved here.
Let's get started with the feature engineering parts of it, to start with a real world example here. This is a real world example inspired by like Uber Eats, imagine you have an Uber Eats app and you search for your favorite restaurant, you find the dishes, you place the order. Let's say you're not satisfied with the ETAs that are reported, and you want to build a model to give you more accurate ETAs, so the first things that come to mind is you need additional data features. You start thinking of what you can make use of, a few things that come to mind, how large is the order, the number of dishes you ordered, how busy is the restaurant at that moment in time, how quick is the restaurant generally speaking and what are the traffic conditions at that point in time?
Michelangelo Palette is essentially a platform that makes you easily create these features for your production models. It turns out that managing features, in our experience, is one of the biggest bottlenecks in productizing your ML models and why is that? The first problem is starting with good features, there's abundant data everywhere and data scientists or engineers just wish they had features ready to go so that they can use it further sort of models, so they struggle with that. Even if they end up finding or creating features for their experimental models, it is very difficult to make that data work at production and at scale, and also in real time environments where low latencies are expected.
The third very relevant problem to ML is a lot of training is done in an offline way, and solving often happens, at least at Uber, in real time environments. It is absolutely important to make sure that the data that you're using in real time, at serving time, matches the data that is used at serving, a previous presentation also hinted at this. A lot of times this is done in an ad hoc way, which can result in the so called training/serving skew, which is extremely hard to debug in our experience and should be avoided.
The fourth area, which makes feature engineering generally difficult, and we're seeing more and more of this, is a shift towards real time features. Features more and more are based on the real latest state of the world, and traditional tools simply don't work. Data scientists engineers are used to working with tables and SQL, and this world looks very different, there's Kafka streams, there are microservices.
Palette Feature Store
This is where palette comes in, the first important component of the palette feature store is the feature store. Our goal there is to be a centralized database in the company, to save your curated as well as crowd sourced- by crowd sourced, I mean, features contributed by various teams across Uber - features available in one single place. You can think of it as a one stop shop, where you can search for features. In our case, the restaurant features, trip features, rider features, in one single place, when you don't find those features, we will provide you the tools to create new features and features that are production-related. I'm going to talk a little bit about that in a second.
The other important goal is for teams to share features, often we find various teams are building the same features again and again, like the trips that a driver took daily. You would find 10 different versions of this feature, so not only is this a extremely redundant waste of resources, but it is also important to share the underlying data so that models are really acting on the same data consistently and producing better results.
And the fourth goal is to enable an ecosystem of tools around the feature store, around the features. For instance we're working on Data Drift Detection in real time and also offline, we're also sort of experimenting with automatic feature selection abilities, so that not only will we help you search for features, but also let you point your labels to our feature store and we can help you pick the features that are related to the labels or have an impact on the labels. This is work in progress but this is sort of our eventual goal.
Feature Store Organization
A little bit about how the feature store is organized. At the highest level, it's organized with the entities, so we're talking about the riders, the drivers, restaurants, and so forth. The second level is feature groups, these are logical groups of features that are associated with each other. Typically, these are features or data originating from the same pipeline or job. The third is the feature name, the data attribute name and the fourth is a thing called a join key. It's meta information in the feature store that tells the feature store how to look up that feature, so if you're talking about a restaurant, the restaurant ID is an example of a join key. There's a palette expression here, this is how you would uniquely identify every feature in the palette feature store. Starting with the restaurant real time, this is one feature name, orders placed last 30 minutes and restaurant ID.
Behind the scenes a little bit, how is it implemented? It's supported by a dual data store system, you can hink of it as a lambda architecture, but not quite so. The offline data system is based on warehouse used at Uber, which is Hive. It essentially saves snapshots of features, daily snapshots of features, it's primarily designed for consumption by training jobs. It can consume features at bulk at any given point in time, so the concept of time is really important here. It's designed for bulk retrieval, you can access any number of features, and the system joins these features for you, and stitches it into a common feature vector.
The offline store is paired with an online store. The purpose of the online store is to serve those same features in real time in a low latency way, today we use a KV store there, Cassandra in particular. Unlike the offline store, which says historic data, the goal of the online store is to serve the latest known values of these features. Behind the scenes, we carry out the data synchronization between the two stores, so if you have new features that you ingested into the offline store, they will automatically get copied to the online store. If you ingest real time features into the online store, it will get ETL back to the offline store. That is fundamentally how we achieve the consistency of data online and offline, it's the training/serving skew thing that I talked about earlier.
Moving on a little bit, going back to the example I started with, we want to build this model. It's a simple model, we want four features. How large is the order? Is that something we can build in the feature store? It doesn't seem like it because it's very specific to the context of what the user ordered, so it's probably an input feature, in our world, it's called a basis feature. It comes in during the request or it's present in the training table. How busy is the restaurant? That's something we should be able to look for in the feature store. Quick, is the restaurant same thing here? Busy is the traffic. These seems like generally good features applicable to a wide variety of models.
Creating Batch Features
Let's see how we can go about looking for these. We do go to the feature store and you find they're not there, so now the feature store offers you options to create these features, let's take a look at what are the options we have. Starting with the batch features, these are the most popularly used just because they're easy to use. There's a warehouse, there's a SQL query you can express ETAs with, the general idea is that you would write a Hive query or you would write a custom Spark job, and you would put in a little bit of meta information in the palette feature store and we would automatically productionize the pipelines for you.
Let's walk through this example, there was a feature, "how quick is the restaurant?" This feature doesn't strictly need to be real time in nature, so what we did was we found some warehouse tables that had this information, we decided to run a query to simply aggregate some of this information and went into this feature spec here and we wrote the small snippet of Hive query and the palette feature store using Uber's pipeline workflow infrastructure known as Piper. We automatically created the Piper job for you, not only did we do that, we also automatically created the alerts and monitoring so that if there's any data outage, the on-call would be alerted, so this is all built in out of the box.
The way it works is the feature spec, the job that it produced, started ingesting the data using the Palette APIs into the offline feature store. From there, it was automatically copied to the online store and then this whole feature store system is both available for training and scoring. Similarly, we have support for real time or rather near real time features. Here, we leverage Uber's near real time competition infrastructure, Flink, in particular, is a first class citizen here. The way it works is either you can write a Flink SQL that works on a stream, a real time stream, perform some transformations and produce those features into the feature store. Going back to our example, how busy is the restaurant? It turns out that there is a Kafka topic which is publishing some of this data, so it might be interesting if I were to build a feature out of this. It turns out however it's like a minute aggregation and I need to turn it into a five-minute aggregate.
I use Flink, I perform some aggregation via Flink in real time using streaming SQL and then I turn it into a feature. In this particular case, I'm calling the feature nMeal, it's a restaurant feature, it can be looked up at our ID. The way it works is very similar to the batch case where I go into the feature spec. I know I don't have to write a custom Flink job, although it's supported for more complex sort of feature engineering. Then I can do with a simple sort of Flink SQL, Uber's Flink system offers a service model where you can create a job on demand. We use the palette system to sort of automatically produce the production pipeline, compute these features, ingest it in the online store so unlike the batch store, you first ingest it into the online store. Very symmetrical to the batch store, we automatically ETL this data behind the scenes to offline store, so that it's available for training.
Whenever possible, we also support backfills, which means that if you just onboarded any real time feature, you can make it available for offline for training, that is a feature that we leverage from Flink, the underlying Flink support. For training and serving, the flows are similar, they specify the features that they want, they all get sucked in from the appropriate stores. The training and serving time, they get merged together and the feature vector is shipped for training and serving.
Bringing Your Own Features
Moving on a little bit, what if your features are so odd that you cannot use any of the palette features store tools? This is what we call bring your own features or external features. The expectation is that the customer is managing their own online and offline data here, so that is the caveat. This is like using palette feature store in an unmanaged way, meaning that the features are still registered in the feature store. The feature store still knows how to read these features, but the features are routed to external end points for retrieving them.
Going back to our example, how busy is the region? There was no warehouse table, there was no Kafka topic that had this information, but there was this one micro service that talked to a system outside of Uber to get this information and maybe I can make use of that for this. Here we're specifically talking about the traffic conditions. What I decided to do is I went into the feature spec and I put an entry for a service endpoint, which I know has the traffic information. The small little DSL that we give you from the RPC result, you can extract the features and the features are then made available for serving.
Remember that managing this data in this mode is my responsibility, which means that I decided to log the data, their RPC results and ETL them into this custom store here, and so that the data is available for offline training. I do run it for a bit, a couple of weeks till I had enough training data.
Palette Feature Joins
I mentioned a few times how the features are joined. At the very high level, the algorithm simply is that you start with your input features, recall from the example, there was an order. There was the number of them in orders, the number of dishes, for example and the various keys were supplied as part of the basis training job. The algorithm essentially takes this, joins it against all the palette features, the three different features that we talked about, and produces the final table, which is used for your training. Scoring is very similar, but unlike table format here, you're working on conceptually fewer rows at a time, essentially and logically, palette does the same exact operation. This is a sort of critical function of the palette store and we've invested a lot in getting this right from a scalability standpoint. Essentially, you want to take feature rows and join it, not only using a given key, but also a given point in time.
If there was a feature state from a month ago, you want to exactly state states from that point in time, so it's very important to get this right. Scalability is extremely important, you end up joining billions and billions of rows during training, we've put a lot of engineering effort there to optimize this process. Serving is similar, features are scattered across the board, multiple feature stores. There's a lot of parallelism, caching, that goes into ensure that features are available, can be retrieved in single digit P99 latencies, for example.
That was a quick example of how we put together these features, so we've used this as an input feature and order, and meal came from a near real time feature. There's a prep time feature, which came from batch and busy, we did, bring your own features, but the question at this point is, are you done with the end-to-end feature engineering? Turns out that the answer is no, because there are still customizations you need to do before those features can be consumed in the machine models. There is model specific feature engineering, or there could be chains of features engineering that you may apply. For example, you may look up one feature from palette, and use that to look up another feature, so we've built a framework called Feature Transformers, which lets you do the last part of feature engineering. I'm going to hand it over to Eric [Chen] who's going to talk all over that.
Feature Consumption
Chen: As Amit [Nene] just mentioned, the ways to consume those features are actually pretty arbitrary, so let's just really look into the problem and see what we have. We have four features here, one is the number of orders, as we know it's coming from your input, we call that basis feature. Not too many troubles over there, the other three actually, all very troublesome. Number of meals and meal do represent how busy this restaurant is, let's look into the whole querying feature transformation chain of it. The input, we have the restaurant ID, so not too hard; you can make a query, get a one level query, you can get your nMeal based on our syntax. Prep time, because usually for some new restaurant, there's no prep time, so for this particular case, you actually need two steps. The first step, you can still go to the feature store, get the prep time, but it could be actually a null. You need to introduce some impute, feature impute, so one possible way is, I'm going to use all the restaurants in the same training set and use the average as the prep time for this restaurant, so you need some feature impute after you grab the feature from the store.
The busy of the region is actually even more complicated because that is not a feature associated with the restaurant; instead, it's associated with the region. If you look into this, based on the restaurant ID, we can find the location of the restaurant, which has a lat/long. Then we need to do a conversion into the region, so not too complex, you can imagine this is a geohash. Based on the lat/long, I can get a geohash, and then geohash this entity. Based on this entity, I need to do another round of query to figure out how busy this particular region is. Here even though we're only trying to transform three features, these features are taking sort of like an arbitrary pass to make your final feature available, get into the model. How do we make these things happen?
Michelangelo Transformers
In order to answer this question, let's just take one step back, trying to understand what is a model. This concept is not really new, it was introduced by Michelangelo, we actually stepped on top of Spark ML framework. They have this thing called a Transformer Estimator pattern, so for a particular transformer, that is one stage in your whole pipeline, that will take your input. It will modify or add or remove some fields in your record, so it's doing some manipulation of your data.
What really is a model? A model is basically just a sequence of those transformations. Those transformations could fetch some feature from palette feature store, do some feature manipulation or model inferencing. What Michelangelo extends from, standard Spark, actually made them serverable from both online and offline structures. In the offline way, this actually is exactly the same, that is the spark transformer, but in the online way, they behave differently, we introduce some extension for it to make it happen. Estimator is also part of this framework, usually it's called an Estimator Transformer Pattern, so estimator is used during the training time, during the fit that will generate a transformer and you have a sequence of a transformer, which is what's used in the serving time.
Let's just try to see what is really there, for this particular talk, we're trying to focus more on feature engineering. Only for feature engineering side, for feature and consumption, there are already several types of it. Feature extraction, that's the palette feature store we talked about, it's also feature manipulation, for example, I have the lat/long, I need to translate it into the geohash, that is a UDF function, defining my things. Feature impute; you also can sync that as a feature manipulation, while if you write here, scala line, it's more like saying, "If now, use this value, otherwise use the other way." so it's some code snippet in some sense.
The other two things are more centric on the modeling side, I'm actually not going to touch too deep inside. If you have categorical feature, you need to convert them into numerical values, you have linear regression, you may want to turn on one-hot encoder. If you have binary classification, you may want to define your decisions rationally, all these things can be seen as transformers as well.
Here is a real example where extend the standard Spark transformers. On the estimator side, that's actually exactly the same, nothing really special, the only special thing happens on the transformer side. From the transformer side we use standard Spark and new model things like MLreadable, writable, the only differences is here; it is a transformer, MA transformer stands for Michelangelo transformer. What happened is it actually introduced a one function called score instance, that's all used in the online case. The input is nothing related to data frame at all; it's purely just a map and output is also a map.
Then the question is, because we need to serve the same in the online/offline system, how do we guarantee the consistency? Usually, the way to write a transform function is define this as a UDF and register that UDF in the map function here. That's how you can guarantee the online and offline experience: consistency, because under the hood, they're using the same line of code all the time, then we might have [inaudible 00:26:20] on top of it to make it happen.
Palette Retrieval as a Transformer
Let's get deeper into two examples we want to understand. We talked about feature retrieval, we have that particular syntax, we want to get the feature out of the feature store. We have feature manipulation, for example, convert lat/long into the geohash, figure out the feature impute. Let's just do one by the other, first of all, let's try to understand what the palette feature transformer is. More or less, this is the syntax we already introduced in our previous page, we're talking about in this particular transformation stage we are interested in.
Here is what we're trying to do, in order to do the things in the transformer patterns, we need to figure out for each transformer what type of things we want to do. And the idea here is, we want to [inaudible 00:27:22] between palette feature retrieval and feature manipulation. Here are some examples, for the first feature transformer using the restaurant ID, I'm trying to get the number of meals, that's for the first feature. For the second feature, I'm using the restaurant ID trying to get the prep time, but for the third feature, how busy the region is, I can now finish it. The first step is I'm just going to get the lat/long for this restaurant. Imagine there's an interleave step, after that, I'm going to do another round of feature retrieval, then I can see I have region ID. I'm going to query from that service feature and figure out how busy this feature is. Then this one needs to be interleaved with a feature manipulation. Feature manipulation in our system is called a DSL, because that's a type of domain specific language. The idea here is you can say how I want to transfer a feature, remember, we said we get a lat/long, we want to transfer feature into the region ID so in the next step, we can use the region ID do another round of query, so this transformation can be written as this.
I'm trying to get the region ID, the way to compute it is we provide some built in function in our system that's really just a function that you can use these two features you specified in your previous stage, and then that will translate that into this feature. Similarly, after we grab all the features in the system, we want to do a bunch of impute feature manipulation. We clean up the prep time if it doesn't exist, we're using that as a numerical value. If it doesn't exist, it will fill that with the prep time of it, the average prep time of it. The number of meals, we're only going to just use that as a numerical value so we'll convert that as a numerical number of orders, the similar thing, then the busy [inaudible 00:29:30], similar thing.
This is called an estimator, why is that? It's really because of this fallback logic, because when we need to do the fallback, where are your fallback values coming from? It's actually coming from your training dataset. You want your fallback values the same way as your training and serving time, so that's why it's considered as an estimator. Based on the estimation stage, it will fit through your model, understand all the stats, that becomes the transformer to use. Behind the scene, this one actually is going through some code gen things, so all the things you see or hear [inaudible 00:30:10] now will be converted into some code snippet, and go through some compiler, and becomes Java byte code during serving time. That's where we can guarantee the speed, we don't do the parsing for every single query you have.
Let's now try to just pull the picture together. We have number of meals, we have prep time, we have the busy scale, we're trying to interleave them with a two palette feature retrieval, and two DSL transformer feature manipulations. The first stage, we're going to just query number of meals, prep time, and restaurant, lat/long from the restaurant ID. This manipulation, we're going to change the lat/long into a region ID. In the second round, we're going to, based on the region ID [inaudible 00:30:58] business, and final stage we do some cleanup of all the features. After this is done, the things after this is actually the standard training part, so you start talking about string indexers, start talking about one-hat encoder. If it's a binary classification, you may have one estimator at the end trying to fit your affluence score, fit your decisions threshold based on [inaudible 00:31:21]. That's more or less the whole picture about how can we make this pipeline ready.
Dev Tools: Authoring and Debugging a Pipeline
But remember, we are a platform, we are not an engineering team, so we cannot just code this one and say, "Hey, done," because we have to handle all these things daily from different customers. How can we provide a tool for them so that we can give this power back to them, so that they can, without help from us, do all this work by themselves? The idea is, we already talked about the Hive part coming from Hive query or a Flink query. Here it's about, how can you represent this as customized pipeline in an interactive way?
We actually leverage it directly from the Spark. Spark has this MLreadable, MLwritable thing so it can introduce the civilization, de-civilization. We gave users a tool where they can describe the pipeline they want, in this particular case, that's interleaved between palette retrieval and DSL estimator, then this is part of the regular linear equation. You can fit the model that you can use the fit of the model to try on your data frame, then Michelangelo will provide you with a way at upload store so you can upload. The interesting part is you actually can upload, too, you can upload both the model you train for your one-off test, and also the model you want to train, that's how you can do re-training through the system. We have a work saved flow system behind the scene which does all the optimization behind this simple line of code to make it scalable and re-trainable.
That's more or less all we have, and trying to highlight the takeaways we have. We talked about a particular feature engineering platform. There are two pieces, one is, how do you manage? How do you organize all these features, especially for two stores, online and offline? Also, we talked about how we're going to manipulate those features, and for the offline case, it's many joins. Both online and offline are about speed, so online and offline, they actually have a different way to optimize the queries to make it happen, they also need different stores to make it happen as well.
About the transformers, an actually even bigger level of transformers is MA Transformer, where you introduce the function score instance, that's the place we guarantee online and offline consistency. I actually would phrase the sentence another way, "It's a guaranteed, controlled inconsistency." What do I mean? Feature store, we said, in the online store, it implies that I'm interested in the feature right now. In the offline case, I'm saying, "I'm interested in the feature when this particular record happened." That's indeed not online, offline consistency; it's online, offline, controlled inconsistency. How can we make all these things happen? It's actually all modeled by our transformer framework. We also do all these pipelines behind the scenes, so we provide all the out of box reliability, monitoring, to put the operation back to our users.
Michelangelo, as a whole, is not only about feature engineering, if you go there, you can hear other stories. For example, how do we do [inaudible 00:35:07]? How do we do model understanding? How do we do other things, like Python-related modeling? There are all a lot of interesting stories there.
Participant 1: A quick question is, what if teams use TensorFlow to build a model? How do they leverage this platform?
Chen: TensorFlow is actually hiding behind this transformer framework, so your TensorFlow model can be hooked up with any other transformation stash by yourself, that's what happens behind the scenes. I think you also probably want to say TensorFlow has some feature engineering stuff as well. TensorFlow feature engineering usually is about [inaudible 00:36:12] coding. It's a different feature engineering from the transformer engineering I talked about. It's a different scope of it, it's fused inside TensorFlow part.
Participant 2: I noticed that you had different tools for the feature generation part- you had Hive QL, you had Flink QL. As we go further down, you had Spark for the transformers and the estimators. Have you guys thought about having a unified API, something like Spark Structured Streaming, which sort of works across the board? That way you have data frames to work from start to finish?
Chen: Unified API, the way we're seeing that is actually not a unified API, we see that as a package of APIs. The way we're trying to open this is through IPython Notebook, because we are trying to unify the experience of mainly the terms. When you say, palette feature, it's supposed to mean exactly the same thing when you're trying to do Hive query or when you trying to join them. We're actually at the stage trying to unify all different Python packages under the same naming patterns. We're exposing them all through this IPython Notebook provided by Uber [inaudible 00:37:38], so that's how we are unifying everything.
Nene: I also want to add that these systems that you mentioned, they all have their own sweet spots. Hive will thrive at queries at large scale, Flink will thrive at processing Kafka streams in near real time and then the feature [inaudible 00:37:57] here has a kind of slightly different goal. These systems have matured and they all have their optimal place. We try to leverage what is available in Uber, and make it available via a calm and sort of central place.
Participant 3: I have actually three questions. The first question I wanted to ask is, how often does it happen that a feature is being used across teams at Uber? I think the whole purpose of this platform is to feature sharing, and in my personal experience, outside a team, there is very little feature sharing that is happening. That's my question one. My second question is that when you build a platform like this, how do you prevent the proliferation of features created by different teams when they are exactly the same features, but with a different name. Sometimes they could be exactly the same, sometimes they could have partial correlation, that's question number second. My third question is, you said that there are a bunch of applications for which accessing the feature online cannot be done by a key value store, so you let people have their own custom solution. I want you to describe what are the use cases where you have to use custom solutions, because I can think of three where key values will not work.
Nene: You've got a lot of questions there, I will very quickly answer you in just one minute. I think the first part was about feature sharing. That is indeed very much the goal and although there are some trends where teams contribute to the feature store, but they use their own features, that is true, we see that, but there are teams who actually do actually use the same features, so in our case, it has actually been true. Not to the extent that we'd like, but that is definitely a goal. Now, this is a complex problem and part of it is technical, part of it is like process, and how teams work with each other, but one of the things we have found is that there's a trust element there.
Once you build your ecosystem where you can actually trust the features, where you actually build the tools to show the data distribution, the anomalies, how reliable it is, the confidence of using that feature actually increases, that is what actually we see. If you just throw some key values or features there and you don't know anything about it, that is where the problem originates, in my experience. We are actually focusing on actually building the tooling so that users start trusting the features more.
Another example is the Auto Feature Selection tools that we've been building, not our team but a related team. We are actually finding that unrelated teams are finding features built by the other team by this particular tool, which is actually showing, "Hey, this is a feature that is correlated to your labels.", so they are actually finding it interesting and then coming to us and saying, "How can we use this? Who's owning the pipelines?" Some of it is really related to just having poor tooling and once you invest in the tooling that ought to change, is my view. The third question was pretty complex. I don't know, maybe we should just discuss it like offline.
The tool we're actually building for the automatic feature selection actually shows redundancies across features, so it's a work in progress. But that is one [inaudible 00:41:32] step. Without such tools, you're at the mercy of just reviews, so every check into the palette feature store actually goes through a review. We try our best there and we try to enforce things and entities and rules, but sometimes because it's purely a human process, things can sort of slip through the cracks.
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arrow-left An arrow pointing left. Mercedes-Benz C-Class
28 combined mpg
View all 2015 Mercedes-Benz C-Class specs .
Excellent cabin materials in sedan
Strong acceleration in C300 or C400
Intuitive multimedia system
Generous standard safety features
Well-executed folding backseat
Improved brake-pedal feel
Somewhat numb steering
Backseat still low to the floor
Backup camera should be standard
Air suspension brings little difference in ride quality
Aesthetics of console-mounted display screen
11 trims
C 300 4MATIC
C 300 4MATIC Luxury
C 300 4MATIC Sport
C 300 Luxury
AMG C 63
Our 2015 Mercedes-Benz C-Class trim comparison will help you decide.
Sedan redesigned for 2015
Turbocharged four- or six-cylinder engines
Rear-or all-wheel drive
Available air suspension
Coupe versions carry over from previous generation
2015 Mercedes-Benz C-Class review: Our expert's take
By Kelsey Mays
The 2015 Mercedes-Benz C-Class is no all-out sport sedan, but it's one hell of an appealing luxury car.
If you want a taste of the good life without eviscerating the kids' college fund, entry-luxury cars like the C-Class have answered that kind of call for more than two decades. Or not: The outgoing C-Class sedan sputtered into last place in 2013's $46,000 Sport Sedan Challenge (read it here). That was a turbocharged four-cylinder C250 Sport, and we drove a V6-powered C350 soon after that didn't redeem much. If this is the baby Benz, a lot of bathwater needed draining.
Drain it, Mercedes did.
With the CLA-Class now anchoring the bottom of the lineup, Mercedes moved the redesigned C-Class upmarket in terms of both quality and price. The redesign hits dealerships late this month in all-wheel-drive C300/C400 form, and in early 2015 in rear-wheel-drive C300 form. From luxury to technology, it's eminently more desirable than the car it replaces.
I drove the all-wheel-drive (4Matic) C300 and C400 sedans, both with Sport packages. The C-Class coupe soldiers on in prior-gen form for 2015, but company officials said to expect a coupe replacement within a year or so. Oh, and yes, there will be a high-performance AMG version.
Exterior & Styling
Mercedes-Benz has a habit of tailoring successive generations of the C-Class after the latest flagship S-Class, and the new C-Class follows that mold even more than usual. The sinewy headlights and curvaceous lower grille signal a clear resemblance to the brand's top-flight sedan, which Mercedes redesigned just a year ago. The C-Class is now a little closer in size, too: Overall length is up 3.7 inches versus the outgoing sedan, and width is up 1.6 inches.
Despite the extra size, overall weight is down as much as 200 pounds versus the old C-Class sedan thanks to a chassis that mixes aluminum and steel versus the all-steel chassis in the outgoing car. Officials say much of the new Mercedes-Benz C-Class' body panels — including the hood, doors, roof and trunk — are aluminum, too.
How It Drives
Fire up the C300's 241-horsepower, turbocharged 2.0-liter four-cylinder, and this year's extra standard power is immediately noticeable. The engine has little in common with the outgoing C250's 201-hp, turbo 1.8-liter four-cylinder. It has a flat torque curve — 273 pounds-feet that plateaus from just 1,300 rpm on up — that evokes the BMW 3 Series 328i's excellent turbo four-cylinder. Where the 2014 C250 ran out of steam, the new C300 moves out. Accelerator progression has improved versus the prior car's sleepy pedal, and the standard seven-speed automatic kicks down readily with little gear hunting, particularly in the drivetrain's Sport and Sport Plus modes. You control all that through an Agility Select switch, which also adjusts steering assist, accelerator progression and (in Eco mode) climate control intensity in one convenient toggle.
With the optional Airmatic adaptive air suspension — a first for the C-Class — Agility Select also changes suspension damping. In the all-wheel-drive, Airmatic-equipped C400 I drove, the system's various modes dialed up subtle but noticeable differences in ride comfort. Still, the C-Class lacks the all-consuming isolation that characterizes other Airmatic-equipped Benzes. Ride composure was good over rapid dips and rises on my drive, but Airmatic allowed pockmarked highways to send up a degree of cabin disturbance that was little different from my time in a fixed-suspension C300 Sport; the 1-inch-larger wheels in the Airmatic car (19s) shouldn't be enough to offset the system's ride-control capabilities. There are also fixed (non-Airmatic) base and Comfort suspensions, as well as cushier 17-inch wheels and higher-profile tires, across the lineup. All those will affect ride quality, so test-drive one and decide for yourself. My take? Airmatic, a stand-alone option on the C300 and C400, isn't worth its $1,190 price.
Mercedes-Benz expects fewer than 20 percent of C-Class buyers to opt for the C400, and it's easy to see why. It's a capable car whose turbo, 3.0-liter V-6 makes a stout 329 hp, and the wall-o'-torque (354 pounds-feet from just 1,600 rpm) crests early to send you flying past slower traffic with half the pedal to go. But it's unnecessary; the C300 does this in a pinch, too, albeit with less power in reserve.
It handles better, too. Though Mercedes-Benz claims just a 2 percent difference in weight distribution between the C300 and C400, the latter feels more nose-heavy, with a touch more sloppiness up front in sweeping corners. Flip the Agility Select to Sport Plus, and the Airmatic seems to quell body roll better than even the sport-tuned fixed suspension, which pitches off-center a bit as you throw it around. But the C-Class is still no 3 Series or Cadillac ATS; the tail was loathe to drift in my all-wheel-drive test cars, and the sole rear-driver of the lineup — the non-4Matic C300, available in 2015 — doesn't come with the bigger engine.
Brake-pedal feel, at least, improves a lot versus the outgoing C-Class' mushy pedal. And both the C300 and C400 steer with a light touch, even in Sport Plus mode. The wheel feels precise enough as you dial into a corner, but overall feedback has a degree of numbness at just about every speed. It keeps with today's luxury cars, but it will limit the C-Class' appeal to driving enthusiasts.
If luxury enthusiasts exist, the C-Class will get their trophy. Real wood and metal adorn a cabin that sports first-rate materials from the dashboard to the doors. Anywhere your arms, elbows or knees touch has padding, with available stitched MB-Tex vinyl on areas of the dash and upper doors. Consistent, low-gloss materials are virtually everywhere — a level of quality you don't often find in this class.
My C300 and C400 had comfortable sport seats, save for their smallish lower cushions. Non-sport seats with lower side bolsters are also available. MB-Tex vinyl seats are standard; leather seats are optional. Alas, the backseat still sits low to the floor — an issue in the previous C-Class, too — without a lot of payoff in headroom. Despite claimed improvement in backseat space, legroom for adults remains only adequate.
Cargo volume in the new C-Class is a competitive 12.8 cubic feet, which is up slightly versus the outgoing C-Class (12.4 cubic feet). A new (and standard) 40/20/40-split folding rear seat replaces last year's 60/40-split seat, and it's remarkably well-executed.
Ergonomics & Electronics
Mercedes' Comand system, with a 7- or 8.4-inch screen, is controlled by a rotary knob and a new touchpad. You can spin and click through submenus if swiping on a touchpad isn't your thing.
Standard audio includes Bluetooth phone and audio streaming, HD radio and USB/iPod integration. Mercedes-Benz packages satellite radio with a Burmester stereo, which is optional in the C300 and standard in the C400. The Burmester is pretty darn good.
As of this writing, the 2015 Mercedes-Benz C-Class had not been crash-tested. Standard safety features include seven airbags plus the required antilock brakes and stability system. A drowsy-driver detection system and forward-collision warning system with automatic braking are also standard. That's rare, even among luxury cars. Alas, a backup camera — standard on cars as plebian as the Honda Fit — is optional.
Rear torso airbags are also optional, which brings the total airbag count to nine. (Head-protecting curtain airbags for front and backseat occupants are standard.) Adaptive cruise control with full-stop braking and steering support, which can nudge the wheel left or right to stay in your lane, is part of a $2,800 Driver Assistance Package, which also includes blind spot, cross traffic and lane departure warning systems. It also adds a lane-keeping system that applies selective brakes (not the steering support) to correct your course if you start to drift. It won't intervene if you have a turn signal on, and should you need to swerve, you can overcome it easily with steering effort.
Value in Its Class
The all-wheel-drive C400 gets an EPA-estimated 22/29/24 mpg city/highway/combined, which is decent fuel economy for the six-cylinder competition, but EPA figures are pending for the more popular C300. Mercedes says it expects forward movement on EPA figures, which would suggest better ratings than the current C250/C300, which are as high as 25 mpg combined.
The outgoing Mercedes-Benz C-Class sedan base price started at $36,725 for a rear-drive C250 Sport, including destination. Mercedes says it has no plans to reprise that trim, making the C300 the new base. Rear-drive models will start at $39,325 when they go on sale in early 2015. The extra power and newly standard forward collision warning justifies much of the increase. Other convenience features incur the usual shuffle: A memory driver's seat and power tilt/telescope steering column move to the standard equipment list, while last year's moonroof moves to the options list.
A loaded C400 retail price should top out around $65,000, which is a modest increase over the current C350. I suspect shoppers will accept it. Even with the CLA-Class on sale, the Mercedes C-Class topped 33,000 sales through the first six months of 2014. That makes it the second most popular Benz and fifth most popular U.S. luxury nameplate. Outside the U.S., it's the brand's best-selling car, according to Mercedes. This 2015 redesign should help it stay there — and also move up a few rungs among U.S. luxury shoppers.
Send Kelsey an email
By Volvosleeper from Colleyville Tx, Grand Rapids Mn
Bought it as certified used with 14,000: miles and 3 years old. Absolutely love the car. Now have 60,00 miles on it. Only cost other than routine maintenance has been tires. One dislike is run flat tires. They make no sense. Also did not like the engine shut down at stops ostensibly to save fuel, but I was able to over ride that. And I bought a spare tire and wheel another major issue for me is you cand buy a trailer hitch for a C 400. You can for other C cars, but not the 400.
Very disappointed with this one.
By Kevin from the middle of nowhere from Mertztown, PA
Stylish: I get many complements on the car. Luxurious: I feel like I've "made it" when I drive it. Tech heavy: it took 7 years to figure out how to program in radio stations. Comfortable: can drive for hours without feeling worn out. Those are the good points. Continuous problems with the transmission shifting and hunting for gears then the car goes into limp-home mode until you can pull over and restart the car to clear the code. Tires don't last more than 30K miles with reasonable driving. The head unit for the stereo quit under warranty but we had to pay SiriusXM to restart the radio. The drivers door squeaks so I coat the rubbers with WS40 every couple of months. The grill louvers started squealing like a pig and had to be replaced several times. Now they are telling me it needs an expensive transmission repair.
Best car I ever owned
By Awesome ride from DFW
This car has been the best and most reliable car I have ever owned. My wife got a new car for her birthday or I would not be selling it now.
See all 122 consumer reviews
Latest 2015 C-Class stories
Recall Recap: The 5 Biggest Recalls in December
2015-2019 Mercedes-Benz, Mercedes-AMG Cars: Recall Alert
How to Prevent Scratches, Swirls on a Black Car
By Joe Bruzek
See all 2015 Mercedes-Benz C-Class articles
All model years for the Mercedes-Benz C-Class


Shop a range of print vinyl including SAVs (self-adhesive vinyls) products suitable for displays, floor graphics, window graphics, decals and much more; available in different finishes: gloss, matt or clear. Choose according to the desired type: promotional, intermediate or high performance media. You can also choose the adhestive type: removable, permanent or bubble free. Refine your search and filter by the ink type: solvent, eco-solvent, UV or latex printers.


A PERCH angler has landed one of the most impressive salmon ever sent to Angler's Mail after he tussled with this beak-mouthed male.
Mike Wallis landed the stunning salmon while targetting perch on the lower River Severn with a small soft rubber Tsunami Swim Shad lure.
The 55-year-old retired toolmaker said: "Wow! What a surprise. The lively male was returned alive for his happy ending with his Welsh girlfriends.
"It was an awesome specimen – a fish of a lifetime. It was a complete surprise and I'd never even seen one before!
"I didn't want to waste time weighing him as I wanted to get him back in and on his way upriver ASAP. It was good to see him swim away strongly.
"It had fought really hard and I thought I might have hooked a massive pike until I saw this strange creature come to the surface.
"My friend reckons it weighed about 18 lb which probably isn't far off the mark. I doubt I'll ever catch anything this special ever again.
"I totally advocate catch-and-release and do it for sea fishing too," added Cirencester, Gloucestershire-based Mike.


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Posted on September 29, 2010 by Thomas Roche
Important Moments in Space Junk
Screencap from the Ball Aerospace informational flyer (PDF Link).
An MSNBC article about the launch last Saturday of the US Air Force's Space-Based Space Surveillance (SBSS) satellite gives those foaming-at-the-mouth mildly-left-of-centrists the opportunity to link to an oldie by a goody: the 2008 Space.com article listing the ten most important moments in space-junk history.
Said Top 10 List was published on the occasion of the shoot-down of USA-193, a military satellite launched in 2006 that malfunctioned and was destroyed in February 2008 by a RIM-161/SM-3 ship-based missile launched from the guided missile cruiser USS Lake Erie west of Hawaii in a move probably intended to tell China to quit eyeballin' our space junk.
Poor USA-193 gets no respect, however; it's only number 10 on the list. The other 9 make cheerful reading, including a woman in Oklahoma who got hit on the head by a piece of a rocket, the Australian discovery of supposed alien spheres later said to be water tanks from Gemini V, and the 1978 re-entry of Soviet nuclear-powered satellite Kosmos 954 over Canadian territory, for which the Sov's were billed over CA$6 million, just for starters. There's a significant dose of tragedy, as well, as with the Columbia disaster.
But what's all this about the Space-Based Space Surveillance? No, it's not a branch of the Homeland Security Department of Homeland Security Departments. It's a Department of Defense program coordinated by the Air Force designed to track space junk viewed as potential future threats to "U.S. space assets." The satellite launched this weekend is an SBSS pathfinder, the first of
There's an official informational video about the system here, and you can check out the Ball Aerospace informational PDF that looks like it was crafted at the height of the Kennedy era when guys my Dad's age were chainsmoking Tareytons and wearing black ties with short sleeve shirts.
If you're into that sort of thing, users at YouTube have posted several different videos of this weekend's Vandenberg Air Force Base launch of the Minotaur IV bearing the SBSS pathfinder, from varying sources. One was even shot from someone's back yard.
Oh, and if you're a complete nerd, CelesTrak.com has a completely awesome post-event analysis of the 2008 interception at USA-193 complete with bizarre screencaps straight out of a flight simulator circa 1983.
Tunguska Crater Found. Maybe.
The Anti-Obama Right Grabs at Anti-U.S. Pakistani Conspiracy Delusion
Pakistani Slam Piece Spreads Panic Over Ft. Calhoun
There's Still-Molten Fuel at Fukushima?
iPad-Controlled Quadricopter Surveys Tuscaloosa Storm Damage
Tagged accidents, lists, space
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Provide comfort for the family of Robert Scholz with a meaningful gesture of sympathy.
Mr. Robert Richard Scholz passed away on Friday, December 28, 2018 at the age of 88 years. Arrangements in care of Heartland Funeral Services, 5226 - 46 Street, Olds, Alberta.
To send flowers in memory of Robert Richard Scholz, please visit our Heartfelt Sympathies Store.
Provide comfort for the family of Robert Richard Scholz by sending flowers.


text:
Алексей Корнеевич Романовский (18 февраля 1918, Таловая Балка — 2 ноября 2014, Киев) — советский деятель. Министр культуры УССР в 1973—1977 г. Депутат Верховного Совета УССР 9-го созыва. Член Ревизионной Комиссии КПУ в 1976—1981 г.

Биография
Родился 18 февраля 1918 года в селе Таловая Балка Светловодского района Кировоградской области в семье рабочего.

Окончил Курсы подготовки педагогов Полтавского учительского института (1937), Киевский государственный педагогический институт им. Горького (1951), филологический факультет, Аспирантуру кафедры истории литературы и искусства при Академии общественных наук ЦК КПСС (1958). Доктор филологических наук, профессор. Член Союза писателей СССР (с 1966). Заслуженный работник народного образования Украины, Заслуженный работник культуры Украины, почетный доктор Киевского государственного лингвистического университета, почётный профессор Национального педагогического университета имени Драгоманова.

Деятельность
С 1934 — учитель начальной школы в селе Таловую Балка Ново-Георгиевского района. В 1937—1939 г. — учитель русского языка и литературы в 5-8 классах средней школы.

В 1939—1946 г. — служил в Красной армии. Участник советско-финской и Великой Отечественной войн.

Член ВКП(б) с 1941 года.

В 1946 году служил помощником начальника отделения Сивашского танкового училища.

В 1946—1958 г. — пропагандист Пензенского областного комитета ВКП(б), начальник сектора пропаганды и агитации Литовской железной дороги, инструктор, заместитель начальника политотдела, секретарь партийного комитета КПУ Управления Юго-Западной железной дороги, заместитель заведующего отделом Киевского областного комитета КПУ. В 1958 году защитил кандидатскую диссертацию.

В 1958—1963 г. — начальник Управления театров и музыкальных учреждений Министерства культуры Украинской ССР; заведующий отделом пропаганды Киевского городского комитета КПУ, секретарь Киевского городского комитета КПУ.

В 1963—1964 г. — старший научный сотрудник, ученый секретарь Института литературы имени Тараса Шевченко Академии наук УССР.

В феврале 1964 — июне 1970 г. — ректор Киевского государственного педагогического института иностранных языков.

В июле 1970 — октябре 1973 г. — ректор Киевского государственного педагогического института имени Горького.

С 15 октября 1973 года по 7 июня 1977 — Министр культуры УССР.

В июле 1977 — декабре 1988 г. — ректор Киевского государственного педагогического института иностранных языков.

Автор более 200 статей по вопросам теории и истории литературы, образования и культуры. Его перу принадлежат монографии: «Из истории подготовки Первого Всесоюзного съезда писателей» (1958), «Знаменательная веха» (1965, 1970), «Партия ведёт» (1967), «Литература Ленинского закалки» (1969, 1973, 1979), "Жизнь во имя будущего (1984).

Потом — на пенсии в Киеве.

Награды
 два ордена Трудового Красного Знамени (1971, 1976)
 орден Отечественной войны 2-й ст. (11.03.1985)
 орден Знак Почета (1961)
 орден Дружбы народов (1986)
 орден Богдана Хмельницкого 3-й ст. (1999)
 орден «За заслуги» 3-й ст. (2008)
 медали

Примечания

Литература
 Депутати Верховної Ради УРСР. 9-е скликання — 1975 р.

Ссылки
 КМ Украины

Депутаты Верховного Совета Украинской ССР 9-го созыва
Кавалеры украинского ордена «За заслуги» 3 степени
Министры культуры Украинской ССР
Члены Ревизионной комиссии КП Украины
Родившиеся в Светловодском районе
Секретари парткомов КПСС на предприятиях и в организациях
Секретари Киевского горкома КП Украины
Доктора филологических наук
Ректоры вузов СССР
Делегаты XXV съезда КПСС


What better way is there to let your holiday memories live on than to turn those precious holiday snaps into custom wallpaper? That's right, create your own wallpaper from your photos. As long as your photo is high-resolution, you can upload it onto our website, enter your dimensions, place your order and *HEY PRESTO* you have your very own custom wallpaper.
For every metre of wallpaper you'd like us to print, we suggest that your image is 1000 pixels. So if you'd like your custom wallpaper to be three metres wide, we suggest your photo is at least 3000px landscape. This can easily be achieved on a DSLR camera, but we advise you check your camera's manual.
However, if you have concerns over the quality of your image, don't worry, we won't print any image that we think won't achieve a high-quality finish. All enquires concerning image quality can be made through our enquiry form.
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You don't have to take our word for creating your very own high-quality custom wallpaper, here's our customer, Andrew's #WallsauceMakeover.
In his own words, he described having his very own custom wallpaper as 'feeling like he's back in Switzerland'.
For more tips and tricks head to our blog post on printing your own wallpaper with Wallsauce.com.


As Calliyah took her first breath, her mother took her last, dying in childbirth. The tragedy was compounded by Calliyah having one leg misshapen and twisted, becoming branded by the villagers as a pariah – a 'Dark One' – who should be taken into the forest and left to die.
Gary would like nothing more than to live in a world of ancient walled cities and dragons. As that wasn't likely to happen during his lifetime, Gary decided to create his own world that, unsurprisingly, is full of ancient walled cities and dragons.
Gary is an amazing author. ... from the first paragraph he had you hooked! Deffinately one of my new favorite authors!
The best book I have ever read. It's a must read.


Uploaded Nilesat 201 predicted beam. Nilesat 201/Rascom-QAF1R launch is scheduled today between 8:45 p.m. and 11:34 p.m. (GMT) from Guiana Space Center.
Many thanks to satlaunch for provided info.
Uploaded Skyterra 1 (MSV 1) footprints. The satellite launch is planned from Baykonur in December 2010.
Astra 1G footprints are updated.
Many thanks to deniszh for the source files.
Uploaded Galaxy 27 at 45East footprint.
Updated NSS 12, AMC 7 and AMC 8 footprints.


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Right to Work conference: 'We can fight, we can win'
Over 900 people packed into the Right to Work conference last weekend. Socialist Worker reports on the key themes of the day and looks ahead to building on broad networks of resistance
Published Tue 2 Feb 2010
The opening session of the conference (Pic: Guy Smallman)
The Right to Work conference of resistance and solidarity was a resounding success. It was the most significant gathering of rank and file workers and campaigners since the start of the recession.
Over 900 people took part in the conference in Manchester – trade unionists, unorganised and unemployed workers, students, pensioners and campaigners.
There was a tremendous determination to forge fighting unity, strengthen rank and file organisation, and to deepen political trade unionism.
Workers came from many of the most important recent disputes. They included the Visteon and Vestas occupations, Fujitsu, South Wales signallers, postal workers, Tower Hamlets College, the London cleaners, bus strikers, Premier Foods, the fight against blacklisting on the construction sites and many others.
Rank and file workers and their struggles shaped the conference. But the politics of solidarity and resistance go much wider than trade unionism.
So there were also important contributions about combating the BNP Nazis and the English Defence League. Anti-war activists including Sue Glenton, the mother of Joe Glenton who refused to fight in Afghanistan, and campaigners against climate change.
The speeches from a delegation of migrant workers from various London campaigns was one of the highlights of the day. They inspired everyone present with their searing contributions against racism and union-busting, and their record of courageous struggle.
The pensions discussion brought together those forced to live on the meagre state pension, and those fighting to defend private and public sector schemes.
Workshops took up all these issues and many more, including how to organise the unemployed, how workers can get a genuine political voice, how to halt the jobs massacre.
The conference became an organising centre with students and college lecturers discussing how to take the battle against education cuts forward.
The president of the UCU lecturers' union cdebated with rank and file lecturers. They discussed with college and school students. All were committed to a fighting programme to defend education.
The opening rally was chaired by Ian Allinson, fresh from recent picket lines at Fujitsu.
Tony Kearns, senior deputy general secretary of the CWU union, said, "I'm really amazed by the turnout here. It shows there's a growing awareness among ordinary people that it's time to come together.
"We're going to see the biggest concerted attack on the working class we've ever seen. There are no mainstream politicians saying anything other than 'there will be cuts, and they will be deep'.
"They'll try to pick off groups one by one – that's why we need to build a coalition to defend the interests of the working class."
He also added that the Copenhagen climate change talks were "the biggest political betrayal of the people of this planet by the world's governments" and that the fight for a million climate jobs was very important.
Clara Osagiede, from the RMT cleaners' campaign, said, "The only way to overcome suffering is to organise. It's a hard task but our battles have shown it can be done."
Pete Murray, from the NUJ journalists' union executive, said, "Figures came out yesterday about the increase in suicides of working people across Britain. They increased for the first time in a decade – and follow exactly the trajectory of the recession."
Vestas occupier Mark Smith said, "When management closed our factory we decided to make a stand. We occupied it for 18 days.
"The amount of support and solidarity we've had has been incredible. And we've been out and supported other workers on their picket lines too.
"That's the only way things are going to change – if people stand together and stand up for each other."
Simon Englert, a student from Sussex university, said, "There's a violent attack on our education. But there are also the examples of people coming together to resist – at Tower Hamlets College, Liverpool university, London Met.
"Students and workers are coming together. At Sussex students stormed the Senate meeting chanting 'strike, strike, strike'. We've formed a joint campaign committee.
"If we're going to oppose the destruction of our education we have to oppose all public service cuts.
"The cuts in education are no different from those faced by bin workers or postal workers. We have to fight together."
The Right to Work conference was an idea whose time had come. It brought together many networks of resistance formed during campaigns and strikes.
But it also showed how broad unity is possible in the battles to come. The Right to Work campaign is not designed to eliminate other organisations and campaigns. It is an attempt to embrace wider unity and to give a voice to rank and file activists.
As an example, it specifically set out to encourage participation, and around 300 people spoke during the day's workshops and plenary sessions.
The challenge now is to create permanent organisation at national and, crucially, at local level to carry forward the struggle.
Tue 2 Feb 2010, 19:19 GMT
Right to work conference voices
Where next for Right to Work campaign?
Right to Work conference: workshop reports


WASHINGTON D. C. AND ANY OTHER GUN FREE ZONES NEED TO IMMEDIATELY ALLOW ANY AND ALL PERSONS THAT ARE LEGALLY PERMITTED TO OWN AND CARRY A FIREARM TO BE ABLE TO DO SO, AND THE SAME RIGHTS HAVE TO BE ALLOWED IN ALL OF OUR UNITED STATES! CRIMINALS AND GOVERNMENTS SHOULD NOT BE THE ONLY ARMED ONES!—AMERICANS HAVE THE GOD-GIVEN RIGHT TO PROTECT AND DEFEND THEMSELVES AND THOSE AROUND THEM!—THIS IS MANDATED IN OUR SECOND AMMENDMENT!!
All of this evil/divisive garbage that is being espoused about our guns and our Second Amendment Rights, the NRA, etc., is all about the banning and collection of firearms, leaving only helpless/vulnerable American Citizens! Some in our government and many other governments around he world realize how much control they could/would have over America and it's citizens if it was disarmed!…. America is what it is because it is armed and impenetrable! We are safer by far as long as we have our guns and gun rights!
All gun free zones are a magnet for potential attacks on helpless/defenseless people that are certain to be victims or potential victims if some sick or evil force shows up to kill/terrorize!
The powers to be, and many of the powerful supporters/financiers of the activists/oppositions to our guns and gun rights are well protected by armed body guards, etc.!… What Hypocrisy! To Hell With The American Citizens!
If the powers that be, and all of the negative, no guns/no gun rights activists and uneducated crowds (cattle) of complainers really gave a rip about safety and loss of lives, there would be no place for them and their pursuits! Common sense dictates that they are all full of crap and evil!