Naru-tard is a derogatory term applied to otaku fans of the anime title Naruto. It is a word play on the combination of naruto and retard. This term surfaced during early 2004 at the beginning of the anime's popularity in America and characterizes the behavior of Naruto fans as being so blindly devoted to the series (with an almost religious cult-like fervor) that they act incredibly stupid and can be confused with retarded (er, mentally challenged) persons.
This is mostly seen on internet forums and chat services such as IRC, where the Naruto series is a constant source of debate. Many narutards dress as their favorite character during conventions or festivals to display their metal head bands, which are worn by characters in the series as a sign of gang affiliation. One characteristic of a narutard is that they will wear just a head band over their street clothes and tell everyone that they are cosplaying. Serious cosplayers who wear costumes that cost hundreds of dollars in raw materials and took weeks to construct generally consider narutards to be anywhere from merely ignorant to downright insulting.
With the license acquisition of Naruto in early 2005 by ShoPro Entertainment, many narutards spawned numerous forum discussions debating the impact of the deal, from the legality of trafficking amateur subtitled copies of the title (something which should be pretty obvious, especially once American distribution rights had been acquired) to a grass-roots petition begging Cartoon Network and ShoPro / Viz to retain the full integrity of the anime during its adaptation into English. However, as with many Japanese series adapted into English, there are legal and other issues which often make it difficult if not impossible to produce a rendition faithful enough to appease purists.
Most narutards are, unsurprisingly, also purists when it comes to adapting Naruto into English. Allegedly, a few narutards boasted of their intention to engage in physical vandalism, DDoS, and other assaults toward ShoPro and Cartoon Network. Other alleged proposals included a petition and hunger strike camping out in front of these companies' offices.
This is mostly seen on internet forums and chat services such as IRC, where the Naruto series is a constant source of debate. Many narutards dress as their favorite character during conventions or festivals to display their metal head bands, which are worn by characters in the series as a sign of gang affiliation. One characteristic of a narutard is that they will wear just a head band over their street clothes and tell everyone that they are cosplaying. Serious cosplayers who wear costumes that cost hundreds of dollars in raw materials and took weeks to construct generally consider narutards to be anywhere from merely ignorant to downright insulting.
With the license acquisition of Naruto in early 2005 by ShoPro Entertainment, many narutards spawned numerous forum discussions debating the impact of the deal, from the legality of trafficking amateur subtitled copies of the title (something which should be pretty obvious, especially once American distribution rights had been acquired) to a grass-roots petition begging Cartoon Network and ShoPro / Viz to retain the full integrity of the anime during its adaptation into English. However, as with many Japanese series adapted into English, there are legal and other issues which often make it difficult if not impossible to produce a rendition faithful enough to appease purists.
Most narutards are, unsurprisingly, also purists when it comes to adapting Naruto into English. Allegedly, a few narutards boasted of their intention to engage in physical vandalism, DDoS, and other assaults toward ShoPro and Cartoon Network. Other alleged proposals included a petition and hunger strike camping out in front of these companies' offices.
High frequency computing is a class of computer programming applications that relate to the processing of high-volume data streams, usually in real-time or near real-time. In general, its purpose is to enable high-speed decision making in a rapidly changing environment. Examples of high frequency computing include automated trading algorithms, high-speed network monitoring and management applications, and dynamic control systems such as fly-by-wire avionics.
This class combines elements of real-time computing, event stream processing and high performance computing, but is distinct since it assumes that a high-volume data stream is being analyzed in real-time.
Characteristics
High frequency computing usually involves real-time processing on data streams with incoming data rates of 1,000 to 1,000,000 updates per second or higher. At these rates, the number of CPU instruction cycles that are available between arriving updates typically dominate the design decisions. Due to these demands, high frequency computing applications generally share a number of common characteristics. The goal is to optimize the number of CPU cycles spent on tasks related to processing of the next arriving update.
Multi-processor systems
Because of their proximity, a machine with 2 or more CPUs can run more efficiently than an equivalent number of single CPU machines. High frequency computing can take advantage of this efficiency, particularly since one or more of the CPUs can be assigned to handle the data update process, leaving the remaining CPUs to handle the processing related to other tasks such as I/O, analytics, etc. Most high frequency computing application are typically implemented on machines with 2 or more CPUs.
Multiple threads of execution
Modern operating systems can handle multiple execution threads with a high degree of efficiency - particularly when more than one CPU is available on a machine. In fact, an application should have at least one active thread per CPU in order to gain maximum efficiency from the available CPU cycles. In practice, isolating the high frequency data updates from other computing tasks such as analytics via a separate thread can greatly simplify the programming task and can help ensure that at least one or more CPUs are dedicated to serving the incoming data updates.
Limited I/O
In keeping with the goal of optimizing the number CPU instructions between data updates, tasks that cause the CPU to wait, such as I/O to storage devices, network interfaces, etc., are avoided to the extent possible. Where I/O cannot be avoided, care is taken to buffer device I/O, and take other steps such as using multiple threads to innsulate the data update process from any unnecessary waiting. In practice, this usually means data is kept in memory rather than written out to disk-based storage or transactional databases such as Oracle or SQL Server. Because transactional databases are often unable to handle more than a few hundred updates per second without significant tuning and/or advanced hardware, high frequency computing applications often rely on non-transactional storage techniques such as file-based storage.
Buffering
Because of the bursty nature of many sources of real-time data, high frequency computing often uses dynamic buffering techniques, such as expandable circular buffers, to cache data between different processing steps. For instance, one thread may be responsible for updating an internal last-value array, i.e. caching the latest update across an array of values. This thread may be fed by data arriving on a network interface. Typically, the network interface process will cache the incoming data into a storage buffer, from which the data update process will draw the data down. The storage buffer reduces the chance that data will be lost because the data update process is busy when data arrive on the network interface. Careful use and monitoring of buffering is crucial in most high frequency computing applications.
Highly efficient locking
The use of multiple, independent execution threads requires synchronization techniques to avoid problems that can arise when different threads share the same memory space. In general, synchronization techniques can be either fast or slow. Synchronization between different processes is usually much slower than synchronization between different threads running in the same process. Therefore, high frequency computing applications tend to run as a single process, with multiple threads using efficient locking.
High speed analytics
Handling the data updates is one part of the problem. Deciding whether to take action based on the data, and doing so in a timely manner is another. The logic that handles these decisions are called analytics. Typically, the analytics in high-frequency computing must also be very fast. Inefficient analytics can bog down the CPU and eventually cause the data update process to slow down or overflow its buffers. For example, a real-time trading application may scan incoming data, and apply a complicated financial model to determine whether there is a profitable trade available. If the analytics are too slow or complicated, the opportunity may disappear before a decision can be made. Analytics can be optimized via employing intelligent approximations, using separate execution threads and deploying the application on multiple CPU servers.
Fixed-length records
Processing streams of fixed-length records, in general, can be much faster than streams composed of variable length records. This is because variable-length records usually require more complex logic to deserialize the incoming data and reconstruct the complex structures described by the data. In contrast, processing a fixed-length record can be as simple as copying a byte array. For this reason, high frequency computing applications tend to use fixed-length records whenever possible.
Mutable strings
One common performance bottleneck stems from the manipulation of immutable strings. This is because any operation that attempts to modify an immutable string generally requires a memory allocation step. When used inappropriately, immutable strings can seriously degrade system performance. Examples of mutable string structures are the StringBuilder class in , and the StringBuffer and StringBuilder classes in Java.
Examples
Event Stream Processing
Algorithmic Trading
This class combines elements of real-time computing, event stream processing and high performance computing, but is distinct since it assumes that a high-volume data stream is being analyzed in real-time.
Characteristics
High frequency computing usually involves real-time processing on data streams with incoming data rates of 1,000 to 1,000,000 updates per second or higher. At these rates, the number of CPU instruction cycles that are available between arriving updates typically dominate the design decisions. Due to these demands, high frequency computing applications generally share a number of common characteristics. The goal is to optimize the number of CPU cycles spent on tasks related to processing of the next arriving update.
Multi-processor systems
Because of their proximity, a machine with 2 or more CPUs can run more efficiently than an equivalent number of single CPU machines. High frequency computing can take advantage of this efficiency, particularly since one or more of the CPUs can be assigned to handle the data update process, leaving the remaining CPUs to handle the processing related to other tasks such as I/O, analytics, etc. Most high frequency computing application are typically implemented on machines with 2 or more CPUs.
Multiple threads of execution
Modern operating systems can handle multiple execution threads with a high degree of efficiency - particularly when more than one CPU is available on a machine. In fact, an application should have at least one active thread per CPU in order to gain maximum efficiency from the available CPU cycles. In practice, isolating the high frequency data updates from other computing tasks such as analytics via a separate thread can greatly simplify the programming task and can help ensure that at least one or more CPUs are dedicated to serving the incoming data updates.
Limited I/O
In keeping with the goal of optimizing the number CPU instructions between data updates, tasks that cause the CPU to wait, such as I/O to storage devices, network interfaces, etc., are avoided to the extent possible. Where I/O cannot be avoided, care is taken to buffer device I/O, and take other steps such as using multiple threads to innsulate the data update process from any unnecessary waiting. In practice, this usually means data is kept in memory rather than written out to disk-based storage or transactional databases such as Oracle or SQL Server. Because transactional databases are often unable to handle more than a few hundred updates per second without significant tuning and/or advanced hardware, high frequency computing applications often rely on non-transactional storage techniques such as file-based storage.
Buffering
Because of the bursty nature of many sources of real-time data, high frequency computing often uses dynamic buffering techniques, such as expandable circular buffers, to cache data between different processing steps. For instance, one thread may be responsible for updating an internal last-value array, i.e. caching the latest update across an array of values. This thread may be fed by data arriving on a network interface. Typically, the network interface process will cache the incoming data into a storage buffer, from which the data update process will draw the data down. The storage buffer reduces the chance that data will be lost because the data update process is busy when data arrive on the network interface. Careful use and monitoring of buffering is crucial in most high frequency computing applications.
Highly efficient locking
The use of multiple, independent execution threads requires synchronization techniques to avoid problems that can arise when different threads share the same memory space. In general, synchronization techniques can be either fast or slow. Synchronization between different processes is usually much slower than synchronization between different threads running in the same process. Therefore, high frequency computing applications tend to run as a single process, with multiple threads using efficient locking.
High speed analytics
Handling the data updates is one part of the problem. Deciding whether to take action based on the data, and doing so in a timely manner is another. The logic that handles these decisions are called analytics. Typically, the analytics in high-frequency computing must also be very fast. Inefficient analytics can bog down the CPU and eventually cause the data update process to slow down or overflow its buffers. For example, a real-time trading application may scan incoming data, and apply a complicated financial model to determine whether there is a profitable trade available. If the analytics are too slow or complicated, the opportunity may disappear before a decision can be made. Analytics can be optimized via employing intelligent approximations, using separate execution threads and deploying the application on multiple CPU servers.
Fixed-length records
Processing streams of fixed-length records, in general, can be much faster than streams composed of variable length records. This is because variable-length records usually require more complex logic to deserialize the incoming data and reconstruct the complex structures described by the data. In contrast, processing a fixed-length record can be as simple as copying a byte array. For this reason, high frequency computing applications tend to use fixed-length records whenever possible.
Mutable strings
One common performance bottleneck stems from the manipulation of immutable strings. This is because any operation that attempts to modify an immutable string generally requires a memory allocation step. When used inappropriately, immutable strings can seriously degrade system performance. Examples of mutable string structures are the StringBuilder class in , and the StringBuffer and StringBuilder classes in Java.
Examples
Event Stream Processing
Algorithmic Trading
The Mexican Hot 100 is a music singles popularity chart issued weekly by TV Azteca and MTV Mexico which ranks songs in Mexico. The chart premiered in the TV Azteca in January 19, 1998 and was made available for the first time via MTV Mexico on February 7, 1998. The chart feautures mostly American songs.
The Mexican Hot 100 is similar to Billboards U.S.-based Hot 100 in that it combines sales via digital downloads, as measured by Nielsen SoundScan, and Mexican radio audience levels as measured by Nielsen BDS. Mexican's airplay chart is the result of monitoring more than 100 stations representing rock, country, adult contemporary and Top 40 genres.
TV Azteca charts manager Geoff Mayfield announced the premiere of the chart, explaining "the new MTV Mexican Hot 100 will serve as the definitive measure of Canada's most popular songs, continuing our magazine's longstanding tradition of using the most comprehensive resources available to provide the world's most authoritative music charts."
Facts
* The artist with most number one songs currently is Timbaland, having three songs to reach the top spot.
* The female artist with most number one songs is Britney Spears, with two tracks having topped the chart.
The Mexican Hot 100 is similar to Billboards U.S.-based Hot 100 in that it combines sales via digital downloads, as measured by Nielsen SoundScan, and Mexican radio audience levels as measured by Nielsen BDS. Mexican's airplay chart is the result of monitoring more than 100 stations representing rock, country, adult contemporary and Top 40 genres.
TV Azteca charts manager Geoff Mayfield announced the premiere of the chart, explaining "the new MTV Mexican Hot 100 will serve as the definitive measure of Canada's most popular songs, continuing our magazine's longstanding tradition of using the most comprehensive resources available to provide the world's most authoritative music charts."
Facts
* The artist with most number one songs currently is Timbaland, having three songs to reach the top spot.
* The female artist with most number one songs is Britney Spears, with two tracks having topped the chart.
AutoZine is an independent online car magazine started in 1997 by Mark Wan of Hong Kong. It offers a goude to all new cars on the market, a blog, pages on classic cars, a very comprehensive technical guide called the "Technical School", news about the worldwide automotive industry, a gallert of cars, and much more.
Autozine Awards
AutoZine gives out its own awards. They are: Car of the Year, Styling of the Year, Concept of the Year, Technology of the Year, Surprise of the Year, Disappointment of the Year, Ugliest Car of the Year, and Worst Car of the Year, but the last three are awards a car maker doesn't want to recieve.
Award winners
*Car of the Year:
**2007 Ferrari 599GTB
**2006 Bugatti EB16.4 Veyron
**2005 Ferrari F430
**2004 Ford GT
**2003 Mercedes-Benz E-class and E55 AMG
**2002 Mercedes-Benz SL
**2001 Alfa Romeo 147
**2000 Ferrari 360 Modena
**1999 Ford Focus
*Styling of the Year:
**2007 Alfa Romeo 8C Competizione
**2006 Citroen C6
**2005 Citroen C4
**2004 Bentley Continental GT
**2003 Nissan March (Micra)
**2002 Lamborghini Murcielago
**2001 Alfa Romeo 147
**2000 Toyota Yaris
**1999 Chrysler 300M
*Concept of the Year:
**2007 Honda FCX
**2006 Mazda Kabura
**2005 Fiat Trepiuno
**2004 Lancia Fulvia Coupe
**2003 Italdesign Brera
**2002 Saab 9X
**2001 Cadillac Imaj
**2000 Bertone Bella
*Technology of the Year:
**2007 BMW Turbosteamer
**2006 Volkswagen-Eaton Twincharger
**2005 BorgWarner regulated 2-stage turbocharging system
**2004 Toyota Prius
**2003 BorgWarner DualTronic double-clutch gearbox
**2002 BMW 7-series
**2001 Saab SVC (variable compression) engine
**2000 Audi Multitronic CVT
**1999 Volkswagen 24-valve VR6
*Surprise of the Year:
**2007 Venturi Astrolab
**2006 Chevrolet Corvette Z06
**2005 Chrysler 300C
**2004 Ford GT
**2003 Smart Roadster
*Disappointment of the Year:
**2007 Chrysler
**2006 Honda Civic
**2005 End of road for Rover and Saab
**2004 MG Rover
**2003 Cancellation of Jaguar F-Type and X-Type R
**2002 BMW design studio
**2001 Jurgen Schrempp (Daimler Chrysler)
**2000 Mercedes CLR Le Mans challenger
**1999 Rolls-Royce / Bentley split
*Ugliest Car of the Year:
**2007 Dodge Avenger
**2006 Toyota bB
**2005 BMW 1-Series
**2004 Dodge Magnum SRT-8
**2003 MG SV
*Worst Car of the Year:
**2007 Dodge Caliber (but a Proton was second)
**2006 Proton Savvy
**2005 Proton Gen.2
**2004 Rover CityRover
**2003 Mercury Marauder
Autozine Awards
AutoZine gives out its own awards. They are: Car of the Year, Styling of the Year, Concept of the Year, Technology of the Year, Surprise of the Year, Disappointment of the Year, Ugliest Car of the Year, and Worst Car of the Year, but the last three are awards a car maker doesn't want to recieve.
Award winners
*Car of the Year:
**2007 Ferrari 599GTB
**2006 Bugatti EB16.4 Veyron
**2005 Ferrari F430
**2004 Ford GT
**2003 Mercedes-Benz E-class and E55 AMG
**2002 Mercedes-Benz SL
**2001 Alfa Romeo 147
**2000 Ferrari 360 Modena
**1999 Ford Focus
*Styling of the Year:
**2007 Alfa Romeo 8C Competizione
**2006 Citroen C6
**2005 Citroen C4
**2004 Bentley Continental GT
**2003 Nissan March (Micra)
**2002 Lamborghini Murcielago
**2001 Alfa Romeo 147
**2000 Toyota Yaris
**1999 Chrysler 300M
*Concept of the Year:
**2007 Honda FCX
**2006 Mazda Kabura
**2005 Fiat Trepiuno
**2004 Lancia Fulvia Coupe
**2003 Italdesign Brera
**2002 Saab 9X
**2001 Cadillac Imaj
**2000 Bertone Bella
*Technology of the Year:
**2007 BMW Turbosteamer
**2006 Volkswagen-Eaton Twincharger
**2005 BorgWarner regulated 2-stage turbocharging system
**2004 Toyota Prius
**2003 BorgWarner DualTronic double-clutch gearbox
**2002 BMW 7-series
**2001 Saab SVC (variable compression) engine
**2000 Audi Multitronic CVT
**1999 Volkswagen 24-valve VR6
*Surprise of the Year:
**2007 Venturi Astrolab
**2006 Chevrolet Corvette Z06
**2005 Chrysler 300C
**2004 Ford GT
**2003 Smart Roadster
*Disappointment of the Year:
**2007 Chrysler
**2006 Honda Civic
**2005 End of road for Rover and Saab
**2004 MG Rover
**2003 Cancellation of Jaguar F-Type and X-Type R
**2002 BMW design studio
**2001 Jurgen Schrempp (Daimler Chrysler)
**2000 Mercedes CLR Le Mans challenger
**1999 Rolls-Royce / Bentley split
*Ugliest Car of the Year:
**2007 Dodge Avenger
**2006 Toyota bB
**2005 BMW 1-Series
**2004 Dodge Magnum SRT-8
**2003 MG SV
*Worst Car of the Year:
**2007 Dodge Caliber (but a Proton was second)
**2006 Proton Savvy
**2005 Proton Gen.2
**2004 Rover CityRover
**2003 Mercury Marauder