Preview ZCWO 1/6 scale 機動部隊系列 PTU Police Tactical Unit – Tong 肥堂 12-inch figure

PTU, also known as PTU: Police Tactical Unit, is a 2003 Hong Kong crime thriller film produced and directed by Johnnie To, starring Simon Yam, Maggie Shiu, Lam Suet and Ruby Wong. The film follows a series of encounters of a patrolling Police Tactical Unit of one single night, where the team tries to help an Organised Crime and Triad Bureau Sergeant Lo Sa (Lam Suet) to retrieve his lost revolver after he was assaulted by a group of triad members.

Lam Suet (Chinese: 林雪) is a Hong Kong actor whose roles are often as a bumbling secondary character (due to his weight and size) providing comic relief from the most intense nature of To’s films. He is perhaps most famous for his various supporting roles in To’s films

ZCWO are proud to announce a new collectible figure. In collaboration with and authorized by Universal Entertainment, this is the Hong kong Movie “PTU Tactical Unit”, 1:6 scale Tong 林堂/肥堂 12-inch figure. Scroll down to see the rest of the pictures.

This is one of the rare times that you see a 12-inch figure with a pot belly. Most times 1:6 scale figures are slim, regular or muscled but seldom fat although this is not the first fat figure ever released.

One must be a big fan to want to collect this haha

Check it out: Even the bullet-proof vest doesn’t go all the way down to protect his belly. Does that mean his belly is invulnerable to bullets? LOL

ZCWO 1/6 scale 機動部隊系列 PTU Police Tactical Unit – Tong 肥堂 12-inch figure will come with two “Tong” head sculpts, ZCWO male normal body with fat body suit, 8 pieces of hands, helmet, fabric PTU beret, level 2 vest, PTU uniform with jacket, black belt, black boots, pouches, radio with holster, flashlight with holder, water bottle with pouch, cellphone, watch, pen, handcuffs, M10 with holster, extendable baton with holder, pepper spray with holder, action figure stand with PTU logo.

Comes with an alternate 1/6 scale head sculpt too

This is a companion piece to the earlier released ZCWO and Iminime 1/6 scale PTU Tactical Unit Sergeant (Sam) 12-inch figure (previewed earlier HERE)

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Released Data Set: Features Extracted From YouTube Videos for Multiview Learning

Posted by Omid Madani, Senior Software Engineer

“If it looks like a duck, swims like a duck, and quacks like a duck, then it probably is a duck.”

Performance of machine learning algorithms, supervised or unsupervised, is often significantly enhanced when a variety of feature families, or multiple views of the data, are available. For example, in the case of web pages, one feature family can be based on the words appearing on the page, and another can be based on the URLs and related connectivity properties. Similarly, videos contain both audio and visual signals where in turn each modality is analyzed in a variety of ways. For instance, the visual stream can be analyzed based on the color and edge distribution, texture, motion, object types, and so on. YouTube videos are also associated with textual information (title, tags, comments, etc.). Each feature family complements others in providing predictive signals to accomplish a prediction or classification task, for example, in automatically classifying videos into subject areas such as sports, music, comedy, games, and so on.

We have released a dataset of over 100k feature vectors extracted from public YouTube videos. These videos are labeled by one of 30 classes, each class corresponding to a video game (with some amount of class noise): each video shows a gameplay of a video game, for teaching purposes for example. Each instance (video) is described by three feature families (textual, visual, and auditory), and each family is broken into subfamilies yielding up to 13 feature types per instance. Neither video identities nor class identities are released.

We hope that this dataset will be valuable for research on a variety of multiview related machine learning topics, including multiview clustering, co-training, active learning, classifier fusion and ensembles.

The data and more information can be obtained from the UCI machine learning repository (multiview video dataset), or from here.


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