Marty Abrams presents Mego™: Iconic Action Figure Brand Mego Returns Exclusive to Target

Mego Corporation is proud to announce the return of the ground-breaking action figure toy line, Marty Abrams presents Mego™, which will be available exclusively at Target this August.

The first wave will include characters from hit properties like Star Trek, DC and I Dream of Jeannie. Each will have a unique serial number and be produced in a limited run of 10,000.

“This has been 30 years in the making and I’m excited to give my loyal fans and their kids something new and amazing to play with,” said Martin Abrams, Co-Founder and CEO of Mego Corporation. “We wanted to recreate a line that provides that nostalgic feeling of seeing a display of action figures they had as a child, and to share that with their own children.”

Marty Abrams presents Mego™ will be revealing the new line at San Diego Comic-Con (booth 830), July 19th – 22nd. Comic-Con guests will have a unique opportunity to purchase a limited number of the new action figures before they launch at Target. All items will be signed by Marty Abrams and a special celebrity guest.

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About Mego Corporation
Mego Corporation was founded in 1954 and became the first company to make action figures of TV and comics superheroes. Led by Marty Abrams, Chairman of the Original Mego Corporation and the godfather of the modern-day action figure, the company continues to successfully build on its history as an innovative leader in licensed Action Figure Toys and Toy Products for the United States and International Markets. Mego Corporation has established its headquarter in Great Neck, NY, with manufacturing facilities in China and Mexico.

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DC Collectibles announces new statues and action figures ahead of San Diego Comic-Con

DC Collectibles, the award-winning line of collectibles from DC Entertainment, will make a huge splash at Comic-Con International: San Diego with exciting new statue and action figure reveals, headlined by a stunning statue series based on Warner Bros. Pictures’ highly anticipated Aquaman film, hitting theaters December 21, 2018.

Making waves just in time for the King of Atlantis’ big-screen debut, DC Collectibles will release three new statues based on the character appearances in the upcoming Aquaman film. Jason Momoa’s Aquaman, Amber Heard’s Mera and Yahya Abdul-Mateen II’s Black Manta will be re-imagined with impeccable attention to detail as individual 12” sculptures.

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2019 releases include a Batman: Black & White Batman statue based on the acclaimed BATMAN: WHITE KNIGHT artwork by Sean Murphy and Batman by Klaus Janson

DC Collectibles will also add new characters to its fan-favorite Batman: The Animated Series (BTAS) action figure line. They include Two-Face, Gray Ghost, H.A.R.D.A.C. and Scarecrow. Each character will feature unique accessories based on their appearance in the animated series.

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Sideshow Collectibles 1/6th scale Mythos Boba Fett 12-inch figure like you’ve never seen before

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“I take it you have no love of the empire.”

Sideshow is excited to unveil the next addition to the Star Wars Collectibles Mythos series, the Boba Fett Sixth Scale Figure.

As a collection, Sideshow’s Mythos series are fine art collectibles that allow the collector to imagine what could have been but maybe never was. Myths meant to be initiated by the eye and completed by the mind.

The Boba Fett Sixth Scale Figure captures the galactic legend of this feared bounty hunter in a never-before-seen way. Boba Fett features uniquely sculpted Mandalorian armor, including his iconic helmet with an articulating rangefinder, a flight suit and vest with body plate armor, knee pads, and weaponized wrist gauntlets. Each aspect of the newly crafted armor is detailed with distinctive Mythos weathering effects, telling the story of many hard-fought battles to capture some of the galaxy’s most elusive bounty. Boba Fett’s unique costume also includes a worn fabric poncho, a kama skirt with utility pouches, ankle spats, and several belts with side and waist pouches.

The Boba Fett Sixth Scale Figure packs a full arsenal of dangerous weaponry, including a removable Z-6 jetpack with articulating rocket boosters, four multi-purpose shin tools, and a tactical knife with an ankle-mounted sheath. He also features a variety of firepower, including an EE-3 carbine blaster rifle, a concussion grenade launcher, a BlasTech DLT-19 heavy blaster rifle with camo wrapping, and a Sacros K-11 blaster pistol. The figure comes with eight interchangeable hands, including a pair of fists, a pair of neutral hands, a pair of grip hands, a right trigger hand, and a left rifle support hand.

Every bounty hunter needs trophies to prove their skill – Boba Fett comes with three shoulder-mounted synthetic-hair Wookiee braids, and three unique lightsaber hilts taken from his fallen targets.

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Related posts:
Sideshow Collectibles 1/6th scale Obi-Wan Kenobi Mythos 12″ collectible figure sneak peek posted on my toy blog HERE
Comparing 12-inch Boba Fett Action Figures by Marmit, Medicom Toys and Sideshow Collectibles (pics HERE)
Sideshow Collectibles 1/6th scale Boba Fett, Han in Carbonite and 12-inch Stormtrooper escorts posted HERE

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Announcing Cirq: An Open Source Framework for NISQ Algorithms

Posted by Alan Ho, Product Lead and Dave Bacon, Software Lead, Google AI Quantum Team

Over the past few years, quantum computing has experienced a growth not only in the construction of quantum hardware, but also in the development of quantum algorithms. With the availability of Noisy Intermediate Scale Quantum (NISQ) computers (devices with ~50 – 100 qubits and high fidelity quantum gates), the development of algorithms to understand the power of these machines is of increasing importance. However, a common problem when designing a quantum algorithm on a NISQ processor is how to take full advantage of these limited quantum devices—using resources to solve the hardest part of the problem rather than on overheads from poor mappings between the algorithm and hardware. Furthermore some quantum processors have complex geometric constraints and other nuances, and ignoring these will either result in faulty quantum computation, or a computation that is modified and sub-optimal.*

Today at the First International Workshop on Quantum Software and Quantum Machine Learning (QSML), the Google AI Quantum team announced the public alpha of Cirq, an open source framework for NISQ computers. Cirq is focused on near-term questions and helping researchers understand whether NISQ quantum computers are capable of solving computational problems of practical importance. Cirq is licensed under Apache 2, and is free to be modified or embedded in any commercial or open source package.

Once installed, Cirq enables researchers to write quantum algorithms for specific quantum processors. Cirq gives users fine tuned control over quantum circuits, specifying gate behavior using native gates, placing these gates appropriately on the device, and scheduling the timing of these gates within the constraints of the quantum hardware. Data structures are optimized for writing and compiling these quantum circuits to allow users to get the most out of NISQ architectures. Cirq supports running these algorithms locally on a simulator, and is designed to easily integrate with future quantum hardware or larger simulators via the cloud.

We are also announcing the release of OpenFermion-Cirq, an example of a Cirq based application enabling near-term algorithms. OpenFermion is a platform for developing quantum algorithms for chemistry problems, and OpenFermion-Cirq is an open source library which compiles quantum simulation algorithms to Cirq. The new library uses the latest advances in building low depth quantum algorithms for quantum chemistry problems to enable users to go from the details of a chemical problem to highly optimized quantum circuits customized to run on particular hardware. For example, this library can be used to easily build quantum variational algorithms for simulating properties of molecules and complex materials.

Quantum computing will require strong cross-industry and academic collaborations if it is going to realize its full potential. In building Cirq, we worked with early testers to gain feedback and insight into algorithm design for NISQ computers. Below are some examples of Cirq work resulting from these early adopters:

To learn more about how Cirq is helping enable NISQ algorithms, please visit the links above where many of the adopters have provided example source code for their implementations.

Today, the Google AI Quantum team is using Cirq to create circuits that run on Google’s Bristlecone processor. In the future, we plan to make this processor available in the cloud, and Cirq will be the interface in which users write programs for this processor. In the meantime, we hope Cirq will improve the productivity of NISQ algorithm developers and researchers everywhere. Please check out the GitHub repositories for Cirq and OpenFermion-Cirq — pull requests welcome!

Acknowledgements
We would like to thank Craig Gidney for leading the development of Cirq, Ryan Babbush and Kevin Sung for building OpenFermion-Cirq and a whole host of code contributors to both frameworks.



* An analogous situation is how early classical programmers needed to run complex programs in very small memory spaces by paying careful attention to the lowest level details of the hardware.

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Please Buy This PowerShell Conference In A Book

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A couple of months ago, M:Ike Robbins (the tech editor of my last PowerShell book (https://www.amazon.co.uk/dp/B073RP2SNZ/ref=dp-kindle-redirect?_encoding=UTF8&btkr=1), sent me mail about a project that he and a few others were planning. The idea was to develop a PowerShell Conference in a book. Mike invited some of the big PowerShell community members to each contribute one chapter. The proceeds are intended to help the DevOps community. Any royalties are to go towards OnRamp Scholarships with the DevOps Collective. Of course I said yes.

Well – now that book is published to which I have contributed a chapter. You can read about the book and buy it here: https://leanpub.com/powershell-conference-book. The book has an impressive list of contributors and is pretty reasonably priced! Of course, if you are feeling generous, LearnPub is happy to enable you to pay more.

The On-Ram Scholarship is a great cause, bringing new people into the DevOps field. You can read about the scholarship here: https://powershell.org/summit/summit-onramp/onramp-scholarship/

My chapter in the book is entitled ‘A Lap Around .NET’ in which I look at what is .NET and some of the things you can do with it.

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Kaiyodo Legacy Of Revoltech LR-054 The Batmobile Tumbler in Gotham City Preview Pics

The legendary Batmobile Tumbler, as seen in the “Dark Knight” movie series, joins the Legacy of Revoltech lineup with a new, luxurious polystone base in the image of a Gotham City street! The Batmobile Tumbler features an opening and closing boarding hatch, rotating tires and movable wings. The base was designed and built by the legendary diorama expert Kazuya Yoshioka – this is a rare opportunity to get an example of his work in your collection!

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Scheduled release: October 1, 2018

Related posts:
Batmobiles (toys) from the Batman Movies posted on my toy blog HERE
1966 Batmobile by Hot Wheels (pics HERE)
Review of DC Comics “Batman v Superman: Dawn of Justice” 1:24 scale Metal Die-Cast Batmobile posted HERE

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Improving Connectomics by an Order of Magnitude

Posted by Viren Jain, Research Scientist and Technical Lead and Michal Januszewski, Software Engineer, Connectomics at Google

The field of connectomics aims to comprehensively map the structure of the neuronal networks that are found in the nervous system, in order to better understand how the brain works. This process requires imaging brain tissue in 3D at nanometer resolution (typically using electron microscopy), and then analyzing the resulting image data to trace the brain’s neurites and identify individual synaptic connections. Due to the high resolution of the imaging, even a cubic millimeter of brain tissue can generate over 1,000 terabytes of data! When combined with the fact that the structures in these images can be extraordinarily subtle and complex, the primary bottleneck in brain mapping has been automating the interpretation of these data, rather than acquisition of the data itself.

Today, in collaboration with colleagues at the Max Planck Institute of Neurobiology, we published “High-Precision Automated Reconstruction of Neurons with Flood-Filling Networks” in Nature Methods, which shows how a new type of recurrent neural network can improve the accuracy of automated interpretation of connectomics data by an order of magnitude over previous deep learning techniques. An open-access version of this work is also available from biorXiv (2017).

3D Image Segmentation with Flood-Filling Networks
Tracing neurites in large-scale electron microscopy data is an example of an image segmentation problem. Traditional algorithms have divided the process into at least two steps: finding boundaries between neurites using an edge detector or a machine-learning classifier, and then grouping together image pixels that are not separated by a boundary using an algorithm like watershed or graph cut. In 2015, we began experimenting with an alternative approach based on recurrent neural networks that unifies these two steps. The algorithm is seeded at a specific pixel location and then iteratively “fills” a region using a recurrent convolutional neural network that predicts which pixels are part of the same object as the seed. Since 2015, we have been working to apply this new approach to large-scale connectomics datasets and rigorously quantify its accuracy.

A flood-filling network segmenting an object in 2d. The yellow dot is the center of the current area of focus; the algorithm expands the segmented region (blue) as it iteratively examines more of the overall image.

Measuring Accuracy via Expected Run Length
Working with our partners at the Max Planck Institute, we devised a metric we call “expected run length” (ERL) that measures the following: given a random point within a random neuron in a 3d image of a brain, how far can we trace the neuron before making some kind of mistake? This is an example of a mean-time-between-failure metric, except that in this case we measure the amount of space between failures rather than the amount of time. For engineers, the appeal of ERL is that it relates a linear, physical path length to the frequency of individual mistakes that are made by an algorithm, and that it can be computed in a straightforward way. For biologists, the appeal is that a particular numerical value of ERL can be related to biologically relevant quantities, such as the average path length of neurons in different parts of the nervous system.

Progress in expected run length (blue line) leading up to the results shared today in Nature Methods. The red line shows progress in the “merge rate,” which measures the frequency with which two separate neurites were erroneously traced as a single object; achieving a very low merge rate is important for enabling efficient strategies for manual identification and correction of the remaining errors in the reconstruction.

Songbird Connectomics
We used ERL to measure our progress on a ground-truth set of neurons within a 1-million cubic micron zebra finch song-bird brain imaged by our collaborators using serial block-face scanning electron microscopy and found that our approach performed much better than previous deep learning pipelines applied to the same dataset.

Our algorithm in action as it traces a single neurite in 3d in a songbird brain.

We segmented every neuron in a small portion of a zebra finch song-bird brain using the new flood-filling network approach, as depicted here:

Reconstruction of a portion of zebra finch brain. Colors denote distinct objects in the segmentation that was automatically generated using a flood-filling network. Gold spheres represent synaptic locations automatically identified using a previously published approach.

By combining these automated results with a small amount of additional human effort required to fix the remaining errors, our collaborators at the Max Planck Institute are now able to study the songbird connectome to derive new insights into how zebra finch birds sing their song and test theories related to how they learn their song.

Next Steps
We will continue to improve connectomics reconstruction technology, with the aim of fully automating synapse-resolution connectomics and contributing to ongoing connectomics projects at the Max Planck Institute and elsewhere. In order to help support the larger research community in developing connectomics techniques, we have also open-sourced the TensorFlow code for the flood-filling network approach, along with WebGL visualization software for 3d datasets that we developed to help us understand and improve our reconstruction results.

Acknowledgements
We would like to acknowledge core contributions from Tim Blakely, Peter Li, Larry Lindsey, Jeremy Maitin-Shepard, Art Pope and Mike Tyka (Google), as well as Joergen Kornfeld and Winfried Denk (Max Planck Institute).

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