Mezco just released these preview pictures recently in anticipation of the New York Toy Fair. Looks like it’s going to be a GREAT year for their One:12 Collective (1:12 scale) series! Some of the figures to be released includes: Ascending Knight Batman… Continua a leggere
Scale 1:12Foto: Minichamps Continua a leggere
The Expendables 3 (sometimes stylized as The Expendables III) is a 2014 American ensemble action film directed by Patrick Hughes, and written by Creighton Rothenberger, Katrin Benedikt, and Sylvester Stallone. It is the third installment in The Expenda… Continua a leggere
Pre-order Toys Works TW001 1/6th scale MadSkull action figure from KGHobby Toys HERE
The second season of the American web television series Daredevil, which is based on the Marvel Comics character of the same name, follows Matt Murdock / Daredevil, a blind lawyer-by-day who fights crime at night, crossing paths with the deadly Frank Castle / Punisher along with the return of an old girlfriend–Elektra Natchios. Charlie Cox stars as Murdock, while Jon Bernthal and Élodie Yung are introduced as Castle and Natchios.
The Punisher is a vigilante who employs murder, kidnapping, extortion, coercion, threats of violence, and torture in his war on crime. Driven by the deaths of his wife and two children, who were killed by the mob for witnessing a killing in New York City’s Central Park, the Punisher wages a one-man war on the mob and all criminals in general by using all manner of conventional war weaponry. A war veteran of the United States Marine Corps Scout Sniper, Frank Castle (born Francis Castiglione) is a master of martial arts, stealth tactics, guerrilla warfare, and a wide variety of weapons.
In feature films, Dolph Lundgren portrayed the Punisher in 1989, as did Thomas Jane in 2004, and Ray Stevenson in 2008. Jon Bernthal portrays the character in the second season of Marvel’s Daredevil as a part of the Marvel Cinematic Universe. Bernthal is set to reprise the role in The Punisher, his own self-titled series.
Toys Works 1/6th scale MadSkull 12-inch action figure aka Jon Bernthal as Frank Castle / Punisher will come with Highly detailed battle damaged head sculpt, Muscular body, Hands x 4 pairs. Weapons: M1911 pistol, Barrett MRAD rifle, M4 Carbine, M134 Minigun with ammo. Trench coat, Vest with Skull logo, black T-shirt, pants, Boots, SKULL logo figure stand.
ETA Q2 2017
Action Figure Review of Thomas Jane Punisher by Art Figures posted on my toy blog HERE and HERE
Art Figures Ray Stevenson Punisher action figure reviewed HERE and HERE
VTS Toys Ray Stevenson Punisher action figure review posted HERE, HERE and HERE
In the years after the third Arkhorian War, hordes of reavers, pirates, and mercenaries roamed the world, their swords serving no cause but themselves or the highest bidder. Across these bloodstained lands rode Arhian, proud, fierce, her guile and wit … Continua a leggere
Posted by Chris Stephenson, Head of Computer Science Education Strategy
The CS Capacity program was launched in March of 2015 to help address a dramatic increase in undergraduate computer science enrollments that is creating serious resource and pedagogical challenges for many colleges and universities. Over the last two years, a diverse group of universities have been working to develop successful strategies that support the expansion of high-quality CS programs at the undergraduate level. Their work focuses on innovations in teaching and technologies that support scaling while ensuring the engagement of women and underrepresented students. These innovations could provide assistance to many other institutions that are challenged to provide a high-quality educational experience to an increasing number of introductory-level students.
The cohort of CS Capacity institutions include George Mason University, Mount Holyoke College, Rutgers University, and the University California Berkeley which are working individually, and Duke University, North Carolina State University, the University of Florida, and the University of North Carolina which are working together. These institution each brings a unique approach to addressing CS capacity challenges. Two years into the program, we’re sharing an update on some of the great projects and ideas to emerge so far.
At George Mason, for example, computer science professor Jeff Offutt and his team have developed an online system to provide self-paced learning for CS1 and CS2 classes that allows learners through the learning materials wore quickly or slowly depending on their needs. The system, called SPARC, includes course content, practice and assessment exercises (including automated testing), mini-lectures, and daily inspirations. This team has also launched a program to recruit and train undergraduate tutorial assistants to increase learning support. For more information on SPARC, contact Jeff Offutt at [email protected]
The MaGE Peer Mentor program at Mount Holyoke College is addressing its increasing CS student enrollment by preparing undergraduate peer mentors to provide effective feedback on coding assignments and contribute to an inclusive learning environment. One of the major elements of these program is an online course that helps to recruit and train students to be undergraduate peer mentors. Mount Holyoke has made their entire online course curriculum for the peer mentor program available so that other institutions can incorporate all or part of it to assist with preparing their own student tutors. For more information on the MaGE curriculum, contact Heather Pon-Barry at [email protected]
|MaGE Program Students and Faculty from Mount Holyoke College|
At University of California, Berkeley, the CS Capacity team is focused on providing access to increased and better tutoring. They’ve instituted a small-group tutoring program that includes weekend mastery learning sessions, increased office hours support, designated discussions section, project checkpoint deadlines, exam/homework/lab/discussion walkthrough videos, and a new office hours app that tracks student satisfaction with office hours. For more information on Berkeley’s interventions, contact Josh Hug at [email protected]
The CS Capacity team at Rutgers has been exploring the gender gap at multiple levels using a longitudinal study across four required CS classes (paper to be published in the proceedings of the SIGCSE 2017 Technical Symposium). They’re investigating several factors that may impact the retention of women and underrepresented student populations, including intention to major in CS, grades, and prior experience. They’ve also been defining an additional set of feature set to improve their use of Autolab (a course management system with automated grading). This work includes building a hint system to provide more information for students who are struggling with a concept or assignment, crowd-sourcing grading, and studying how students think about CS content and the kinds of errors they are making. The Rutgers team will be publishing their study results in the proceedings of the SIGCSE 2017 Technical Symposium. For more information on these tools, contact Andrew Tjang at [email protected]
The team consisting of Duke, NCSU, UNC, and UF have produced and plan to share tools to improve the student learning experience. My Digital Hand (MDH) is a free online tool for managing and tracking one-to-one peer teaching sessions (for example, helping to keep track of how many hours peer mentors are spending with mentees). MDH supports best practice in peer teaching and mitigates some of the observed challenges in taking peer teaching to scale. The team has also been working on ASCEND (Adaptive Student Computing Environment with Natural Language Dialogue), an Eclipse plug-in designed to facilitate remote synchronous peer teaching sessions. Students can share their projects with a peer teaching fellow (PTF) and chat as the PTF leads the student through a session. ASCEND helps instructors better understand current practice by logging all programming actions and textual chats in real time to a database. For more information on these tools, contact Jeff Forbes at [email protected]
Several of the CS Capacity principle investigators will be presenting papers on these new interventions and tools at the SIGCSE conference in March. Faculty from the CS capacity program will also be presenting a panel and roundtable discussion session called “New Tools and Solutions to Address the CS Capacity Crunch.” If you’re attending SIGCSE this year, we hope you’ll join us on Thursday, March 9, from 3:45-5:00 pm.
Given the likelihood that CS undergraduate enrollments will continue to climb, it is critical that the CS education community continue to find, test, and share solutions and tools that enable institutions to effectively teach more students while maintaining the quality of the education experience for students. Faculty from the CS Capacity program will continue to share their solutions and results with the community via CS education conferences and publications.
Sideshow presents their interpretation of one of our most beloved Marvel Comics characters. The Devil of Hell’s Kitchen – Daredevil! Blinded by a mysterious radioactive substance, Matt Murdock discovered that his remaining senses were heightened to superhuman levels.
Sideshow took inspiration for the figure from the classic comic-book Daredevil, dressed head to toe in red with his horned cowl and twin “D” logo on his chest, and then added design motifs from his relatively short-lived Armored suit from the 90’s – while always being mindful not to break up his streamlined silhouette.
The aim from the outset was to create an original design that pays homage to the Daredevil we know and love from the comic books, while bringing a new twist to a classic character.
This quickly became a passion project for Sideshow’s team, and they took every opportunity to add additional “Easter eggs” and symbolism to the design of his tailored fabric and leather-effect costume.
Sideshow Artist, Walter O’Neil said: “Since Matt Murdock’s faith is very much at the heart of who he is, we wanted to try to incorporate some elements of his religion into the suit in very subtle ways. For example, the collar of his suit has a square cut-out in the center to give the impression of an inverted priest’s collar”.
“Inspired by the kneepads of the 90’s armored suit, we created metallic block-shaped design elements with an implied cross-shape that we then repeated throughout the suit. The idea being that we permanently incorporate some of the wrought iron elements of church cathedrals – that we generally associate with Daredevil’s environment – directly into his suit design”.
“Beneath his feet, another cross shape is formed by the negative space in the tread of his boots. There’s also a spade ‘devil tail’ incorporated into the design of his billy club holster that helps further the heaven / hell dynamic”.
Sideshow Collectibles Daredevil Sixth Scale Figure specially features: detailed masked portrait of the Devil of Hell’s Kitchen; Red fabric and leather-like costume with iconic DD on the chest; Sculpted baton holder; Sculpted elbow and knee protectors; Billy clubs with interchangeable parts for multiple display options: Dual billy clubs, Billy club staff, Billy club nunchucks; One (1) pair of sculpted boots; Four (4) pairs of hands: set of fists, set of straight grip hands, set of angled grip hands, set of gesture hands. Sideshow Exclusive: transforming walking stick / billy clubs (see pics below)
Check out Hot Toys Marvel Netflix 1/6th scale Charlie Cox Daredevil 12-inch collectible figure preview pics posted on my toy blog HERE
Daredevil by Toy Biz (pics HERE)
Preview Daredevil “The Man Without Fear” Premium Format Figure by Sideshow Collectibles HERE
The Great Wall is a 2016 monster film directed by Zhang Yimou and starring Matt Damon, who plays a European mercenary in China during the Song dynasty who encounters the Great Wall of China and the Chinese elite soldiers who fight against monsters that attack the wall. The film also stars Jing Tian, Pedro Pascal, Willem Dafoe and Andy Lau. Actress Jing Tian stars as Commander Lin Mae of the Crane Troop.
Threezero 1/6th scale Commander Lin Mae collectible figure, continues our “The Great Wall” movie license after preview pictures were released of the first figure, “The Great Wall” Pedro Pascal as Pero Tovar collectible figure (posted earlier on my toy blog HERE). Commander Lin Mae collectible figure stands approximately 11-inch (28 cm) tall and features highly-accurate life-like likeness to Jing Tian (景甜) as Commander Lin Mae in “The Great Wall” movie. Collectible features realistic synthetic hair, comes with tailored clothing and highly detailed armor outfit with paint application simulating metal armor and weapons. Figure comes with Sword with Scabbard, Two Fan Blades with Scabbard, and Three Pairs of Exchangeable Hands.
Threezero 1/6th scale “The Great Wall” Commander Lin Mae collectible details: Highly detailed figure featuring life-like realism that is authentically crafted with the likeness of Jing Tian (景甜) as Commander Lin Mae in the “The Great Wall” movie. 11-inch (~28 cm) tall, articulated figure with tailored clothing, employing finely detailed paint application simulating metal armor. Black synthetic hair implantation. Collectible figure features the following elements of clothing and accessories: Upper torso armor with chain and fabric cape; Lamellar armor; Shoulder armor; Vambrace; Inner clothes; Waist belt; Pants; Thigh armor and greaves; Boots. Variety of weapons include: Sword with Scabbard; Fan Blades with Scabbard x 2. Collectible figure comes with Exchangeable Hands: One pair of fists; One pair of relaxed; One pair for gripping.
It seems like ThreeZero is releasing all the minor figures first to get people hooked before offering the major stars, Matt Damon and Andy Lau. This way perhaps we get to see the entire cast instead of people just collecting only the main character(s).
Posted by Paul Natsev, Software Engineer
Last September, we released the YouTube-8M dataset, which spans millions of videos labeled with thousands of classes, in order to spur innovation and advancement in large-scale video understanding. More recently, other teams at Google have released datasets such as Open Images and YouTube-BoundingBoxes that, along with YouTube-8M, can be used to accelerate image and video understanding. To further these goals, today we are releasing an update to the YouTube-8M dataset, and in collaboration with Google Cloud Machine Learning and kaggle.com, we are also organizing a video understanding competition and an affiliated CVPR’17 Workshop.
An Updated YouTube-8M
The new and improved YouTube-8M includes cleaner and more verbose labels (twice as many labels per video, on average), a cleaned-up set of videos, and for the first time, the dataset includes pre-computed audio features, based on a state-of-the-art audio modeling architecture, in addition to the previously released visual features. The audio and visual features are synchronized in time, at 1-second temporal granularity, which makes YouTube-8M a large-scale multi-modal dataset, and opens up opportunities for exciting new research on joint audio-visual (temporal) modeling. Key statistics on the new version are illustrated below (more details here).
|A tree-map visualization of the updated YouTube-8M dataset, organized into 24 high-level verticals, including the top-200 most frequent entities, plus the top-5 entities for each vertical.|
|Sample videos from the top-18 high-level verticals in the YouTube-8M dataset.|
The Google Cloud & YouTube-8M Video Understanding Challenge
We are also excited to announce the Google Cloud & YouTube-8M Video Understanding Challenge, in partnership with Google Cloud and kaggle.com. The challenge invites participants to build audio-visual content classification models using YouTube-8M as training data, and to then label ~700K unseen test videos. It will be hosted as a Kaggle competition, sponsored by Google Cloud, and will feature a $100,000 prize pool for the top performers (details here). In order to enable wider participation in the competition, Google Cloud is also offering credits so participants can optionally do model training and exploration using Google Cloud Machine Learning. Open-source TensorFlow code, implementing a few baseline classification models for YouTube-8M, along with training and evaluation scripts, is available at Github. For details on getting started with local or cloud-based training, please see our README and the getting started guide on Kaggle.
The CVPR 2017 Workshop on YouTube-8M Large-Scale Video Understanding
We will announce the results of the challenge and host invited talks by distinguished researchers at the 1st YouTube-8M Workshop, to be held July 26, 2017, at the 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) in Honolulu, Hawaii. The workshop will also feature presentations by top-performing challenge participants and a selected set of paper submissions. We invite researchers to submit papers describing novel research, experiments, or applications based on YouTube-8M dataset, including papers summarizing their participation in the above challenge.
We designed this dataset with scale and diversity in mind, and hope lessons learned here will generalize to many video domains (YouTube-8M captures over 20 diverse video domains). We believe the challenge can also accelerate research by enabling researchers without access to big data or compute clusters to explore and innovate at previously unprecedented scale. Please join us in advancing video understanding!
This post reflects the work of many others within Machine Perception at Google Research, including Sami Abu-El-Haija, Anja Hauth, Nisarg Kothari, Joonseok Lee, Hanhan Li, Sobhan Naderi Parizi, Rahul Sukthankar, George Toderici, Balakrishnan Varadarajan, Sudheendra Vijayanarasimhan, Jiang Wang, as well as Philippe Poutonnet and Mike Styer from Google Cloud, and our partners at Kaggle. We are grateful for the support and advice from many others at Google Research, Google Cloud, and YouTube, and especially thank Aren Jansen, Jort Gemmeke, Dan Ellis, and the Google Research Sound Understanding team for providing the audio features in the updated dataset.
Posted by Amy McDonald Sandjideh, Technical Program Manager, TensorFlow
In just its first year, TensorFlow has helped researchers, engineers, artists, students, and many others make progress with everything from language translation to early detection of skin cancer and preventing blindness in diabetics. We’re excited to see people using TensorFlow in over 6000 open-source repositories online.
It’s faster: TensorFlow 1.0 is incredibly fast! XLA lays the groundwork for even more performance improvements in the future, and tensorflow.org now includes tips & tricks for tuning your models to achieve maximum speed. We’ll soon publish updated implementations of several popular models to show how to take full advantage of TensorFlow 1.0 – including a 7.3x speedup on 8 GPUs for Inception v3 and 58x speedup for distributed Inception v3 training on 64 GPUs!
It’s more flexible: TensorFlow 1.0 introduces a high-level API for TensorFlow, with tf.layers, tf.metrics, and tf.losses modules. We’ve also announced the inclusion of a new tf.keras module that provides full compatibility with Keras, another popular high-level neural networks library.
It’s more production-ready than ever: TensorFlow 1.0 promises Python API stability (details here), making it easier to pick up new features without worrying about breaking your existing code.
Other highlights from TensorFlow 1.0:
- Python APIs have been changed to resemble NumPy more closely. For this and other backwards-incompatible changes made to support API stability going forward, please use our handy migration guide and conversion script.
- Experimental APIs for Java and Go
- Higher-level API modules tf.layers, tf.metrics, and tf.losses – brought over from tf.contrib.learn after incorporating skflow and TF Slim
- Experimental release of XLA, a domain-specific compiler for TensorFlow graphs, that targets CPUs and GPUs. XLA is rapidly evolving – expect to see more progress in upcoming releases.
- Introduction of the TensorFlow Debugger (tfdbg), a command-line interface and API for debugging live TensorFlow programs.
- New Android demos for object detection and localization, and camera-based image stylization.
- Installation improvements: Python 3 docker images have been added, and TensorFlow’s pip packages are now PyPI compliant. This means TensorFlow can now be installed with a simple invocation of pip install tensorflow.
We’re thrilled to see the pace of development in the TensorFlow community around the world. To hear more about TensorFlow 1.0 and how it’s being used, you can watch the TensorFlow Developer Summit talks on YouTube, covering recent updates from higher-level APIs to TensorFlow on mobile to our new XLA compiler, as well as the exciting ways that TensorFlow is being used:
|Click here for a link to the livestream and video playlist (individual talks will be posted online later in the day).|
The TensorFlow ecosystem continues to grow with new techniques like Fold for dynamic batching and tools like the Embedding Projector along with updates to our existing tools like TensorFlow Serving. We’re incredibly grateful to the community of contributors, educators, and researchers who have made advances in deep learning available to everyone. We look forward to working with you on forums like GitHub issues, Stack Overflow, @TensorFlow, the [email protected] group and at future events.