Pre-order SUPER DUCK SET039 1/6 Insect Cosplay Head Sculpt & Costume Set at KGHobby (link HERE)Mantis is a fictional superheroine appearing in American comic books published by Marvel Comics. Mantis made her cinematic debut in Guardians of the Gala… Continua a leggere
Captain Marvel is an upcoming American superhero film based on the Marvel Comics character Carol Danvers. Produced by Marvel Studios and distributed by Walt Disney Studios Motion Pictures, it is intended to be the twenty-first film in the Marvel Cinema… Continua a leggere
|(Jim switches cars on Paul’s module while John looks on)|
|(Up Periscope! Jim created this viewer for the shorter set. It’s popular with
the kids – and with parents, whose backs are grateful…)
The Quinte Model Railroad Show is becoming something of an annual tradition for our group: The show is a favourable location for our eastern Ontario members – and it’s not too distant for those members in Toronto and the Niagara Region. This year, the Workshop crew on site included myself, Paul Raham, John Johnston, and Darby Marriott.
Being Christmastime, this is a show where we let our hair down a little. An elephant grazes in Paul’s pasture, my Stegosaurus and Bovasaurus Rex roam the layout, there are candy cargos in the hopper cars, and John ran a pirate train for his grandson Decker.
|(A holstein/dinosaur cross confronts crossed bones)|
Paul, John, Darby and I enjoyed hanging out with each other and roaming the exhibition halls for bargains and inspiration. Thanks also to Workshop friend Daniel McConnachie for helping out Saturday, and to honorary S-scaler Dennis Rowe for his ongoing encouragement.
Incidentally, this was Darby’s first chance to participate with a module.
|(Darby with his module. The “C” on the wall is not for “Culverhouse”…)|
My Culverhouse module now belongs to Darby and I can’t wait to see how he makes it his own. That process has already started in how he packages it for travel. He has imagineered a clever little arched toboggan under one end of the module which makes sliding it into his car a breeze: So simple and effective.
Currently we are not sure if we have nailed down our appearance at the next Copetown Train Show (February 24, 2019: 10am-3pm) but we’ll keep you posted. In the meantime we hope you enjoy Darby’s photos from the weekend.
Happy holidays, everyone!
Posted by Anurag Batra and Parker Barnes, Product Managers, Google AI
Recently, we introduced the Inclusive Images Kaggle competition, part of the NeurIPS 2018 Competition Track, with the goal of stimulating research into the effect of geographic skews in training datasets on ML model performance, and to spur innovation in developing more inclusive models. While the competition has concluded, the broader movement to build more diverse datasets is just beginning.
Today, we’re announcing Open Images Extended, a new branch of Google’s Open Images dataset, which is intended to be a collection of complementary datasets with additional images and/or annotations that better represent global diversity. The first set we are adding is the Crowdsourced extension which is seeded with 478K+ images donated by Crowdsource app users from all around the world.
About the Crowdsourced Extension of Open Images Extended
To bring greater geographic diversity to Open Images, we enabled the global community of Crowdsource app users to photograph the world around them and make their photos available to researchers and developers as part of the Open Images Extended dataset. A large majority of these images are from India, with some representation from the Middle East, Africa and Latin America.
The images, focus on some key categories like household objects, plants & animals, food, and people in various professions (all faces are blurred to protect privacy). Detailed information about the composition of the dataset can be found here.
|Pictures from India and Singapore contributed using the Crowdsource app.|
This is an early step on a long journey. To build inclusive ML products, training data must represent global diversity along several dimensions. To that end, we invite the global community to help expand the Open Images Extended dataset by contributing imagery from your own hometown and community. Download the Crowdsource Android app to contribute images you’ve taken from your phone, or contact us if there are other image repositories (that you have the rights for) that you’re interested in adding to open-images dataset.
The release of Open Images Extended has been possible thanks to the hard work of a lot of people including, but not limited to the following (in alphabetical order of last name): James Atwood, Pallavi Baljekar, Peggy Chi, Tulsee Doshi, Tom Duerig, Vittorio Ferrari, Akshay Gaur, Victor Gomes, Yoni Halpern, Gursheesh Kaur, Mahima Pushkarna, Jigyasa Saxena, D. Sculley, Richa Singh, Rachelle Summers.
Pre-order Hot Toys MMS517 Luke Skywalker (Deluxe Version) Star Wars Return of the Jedi 1/6th scale Collectible Figure from KGHobby (link HERE)
“I will never turn to the dark side.” – Luke Skywalker
In the epic conclusion of the classic Star Wars saga, after Luke Skywalker has returned to his home planet of Tatooine to rescue his friend Han Solo from the clutches of the vile gangster Jabba the Hutt. The young Skywalker along with his friends and the Rebellion fleet must take down the second Death Star in order to stop the evil Empire from wiping out the last hope to restore freedom to the galaxy…
Hot Toys is very excited to officially present today, the new Deluxe Version 1/6th scale Luke Skywalker collectible figure from Star Wars: Return of the Jedi featuring his iconic attire as seen on Tatooine, Endor, and the second Death Star.
Masterfully crafted based on the appearance of Luke Skywalker in the final chapter of the original trilogy, the new collectible figure features a newly developed head sculpt with remarkable likeness, finely tailored costumes, the helmet and camouflage cloak Luke wore on Endor, a LED light-up lightsaber, an interchangeable lightsaber blade emulating the weapon in motion, a blaster, and a themed figure stand.
Hot Toys MMS517 1/6th scale Star Wars Luke Skywalker Collectible Figure (Deluxe Version) specially features: newly developed head sculpt with authentic and detailed likeness of Mark Hamill as Luke Skywalker in Star Wars: Return of the Jedi | newly painted head sculpt with interchangeable all-new hair sculpture (hair sculpture contains magnetic feature)*** | Movie-accurate facial expression with detailed skin texture | Hair sculpture is equipped with magnetic feature | Approximately 28 cm tall Body with over 30 points of articulations | Five (5) pieces of interchangeable left hands including: fist, gesturing hand, Two (2) open hands, hand for holding lightsaber | Three (3) pieces of interchangeable black-colored gloved right hands including: fist, gesturing hand, hand for holding pistol, battle damaged right hand for holding lightsaber***
Costume: helmet as worn on Endor (with magnetic feature) | camouflage cloak with black-colored belt | black-colored top, black-colored pants | black-colored leather-like belt with a pouch, a hook and a D-ring with silver-colored button clips | black-colored leather-like boots | dark grey-colored tunic with belt*** | light brown-colored sandstorm cloak***
Weapons: LED-lighted green lightsaber (white light, battery operated), lightsaber hilt, green lightsaber blade in motion (attachable to the hilt), blaster pistol, lightsaber hilt with weathering effect***
This Deluxe Version will exclusively include an additional head sculpt with an interchangeable all-new hair sculpture, an extra piece of interchangeable battle damaged right hand, dark grey tunic with belt, and the Tatooine sandstorm goggles and cloak as seen in the deleted scenes.
Accessories: wearable sandstorm goggles***, Two (2) bushes dioramas attachable to the figure base, Specially designed rectangular figure stand with Luke Skywalker nameplate and movie logo
*** Exclusive to Deluxe Version
Release date: Approximately Q3 – Q4, 2019
A PREVIEWS Exclusive!Cable was sent to the future in a bid to save his life when he was infected by a techno-organic virus. Using his formidable telekinetic abilities and cybernetic enhancements, Cable battles super villain foes in his quest for peace…. Continua a leggere
OverviewUSB MIDI controllers (such as Launchpad Mini Mk II for example) are common and often quite low in cost.To interface such a controller with a Eurorack synth system, often a host computer and a MIDI to CV interface might be used. The host co… Continua a leggere
Posted by Xuanhui Wang and Michael Bendersky, Software Engineers, Google AI
Ranking, the process of ordering a list of items in a way that maximizes the utility of the entire list, is applicable in a wide range of domains, from search engines and recommender systems to machine translation, dialogue systems and even computational biology. In applications like these (and many others), researchers often utilize a set of supervised machine learning techniques called learning-to-rank. In many cases, these learning-to-rank techniques are applied to datasets that are prohibitively large — scenarios where the scalability of TensorFlow could be an advantage. However, there is currently no out-of-the-box support for applying learning-to-rank techniques in TensorFlow. To the best of our knowledge, there are also no other open source libraries that specialize in applying learning-to-rank techniques at scale.
Today, we are excited to share TF-Ranking, a scalable TensorFlow-based library for learning-to-rank. As described in our recent paper, TF-Ranking provides a unified framework that includes a suite of state-of-the-art learning-to-rank algorithms, and supports pairwise or listwise loss functions, multi-item scoring, ranking metric optimization, and unbiased learning-to-rank.
TF-Ranking is fast and easy to use, and creates high-quality ranking models. The unified framework gives ML researchers, practitioners and enthusiasts the ability to evaluate and choose among an array of different ranking models within a single library. Moreover, we strongly believe that a key to a useful open source library is not only providing sensible defaults, but also empowering our users to develop their own custom models. Therefore, we provide flexible API’s, within which the users can define and plug in their own customized loss functions, scoring functions and metrics.
Existing Algorithms and Metrics Support
The objective of learning-to-rank algorithms is minimizing a loss function defined over a list of items to optimize the utility of the list ordering for any given application. TF-Ranking supports a wide range of standard pointwise, pairwise and listwise loss functions as described in prior work. This ensures that researchers using the TF-Ranking library are able to reproduce and extend previously published baselines, and practitioners can make the most informed choices for their applications. Furthermore, TF-Ranking can handle sparse features (like raw text) through embeddings and scales to hundreds of millions of training instances. Thus, anyone who is interested in building real-world data intensive ranking systems such as web search or news recommendation, can use TF-Ranking as a robust, scalable solution.
Empirical evaluation is an important part of any machine learning or information retrieval research. To ensure compatibility with prior work, we support many of the commonly used ranking metrics, including Mean Reciprocal Rank (MRR) and Normalized Discounted Cumulative Gain (NDCG). We also make it easy to visualize these metrics at training time on TensorBoard, an open source TensorFlow visualization dashboard.
TF-Ranking supports a novel scoring mechanism wherein multiple items (e.g., web pages) can be scored jointly, an extension of the traditional scoring paradigm in which single items are scored independently. One challenge in multi-item scoring is the difficulty for inference where items have to be grouped and scored in subgroups. Then, scores are accumulated per-item and used for sorting. To make these complexities transparent to the user, TF-Ranking provides a List-In-List-Out (LILO) API to wrap all this logic in the exported TF models.
|The TF-Ranking library supports multi-item scoring architecture, an extension of traditional single-item scoring.|
As we demonstrate in recent work, multi-item scoring is competitive in its performance to the state-of-the-art learning-to-rank models such as RankNet, MART, and LambdaMART on a public LETOR benchmark.
Ranking Metric Optimization
An important research challenge in learning-to-rank is direct optimization of ranking metrics (such as the previously mentioned NDCG and MRR). These metrics, while being able to measure the performance of ranking systems better than the standard classification metrics like Area Under the Curve (AUC), have the unfortunate property of being either discontinuous or flat. Therefore standard stochastic gradient descent optimization of these metrics is problematic.
In recent work, we proposed a novel method, LambdaLoss, which provides a principled probabilistic framework for ranking metric optimization. In this framework, metric-driven loss functions can be designed and optimized by an expectation-maximization procedure. The TF-Ranking library integrates the recent advances in direct metric optimization and provides an implementation of LambdaLoss. We are hopeful that this will encourage and facilitate further research advances in the important area of ranking metric optimization.
Prior research has shown that given a ranked list of items, users are much more likely to interact with the first few results, regardless of their relevance. This observation has inspired research interest in unbiased learning-to-rank, and led to the development of unbiased evaluation and several unbiased learning algorithms, based on training instances re-weighting. In the TF-Ranking library, metrics are implemented to support unbiased evaluation and losses are implemented for unbiased learning by natively supporting re-weighting to overcome the inherent biases in user interactions datasets.
Getting Started with TF-Ranking
TF-Ranking implements the TensorFlow Estimator interface, which greatly simplifies machine learning programming by encapsulating training, evaluation, prediction and export for serving. TF-Ranking is well integrated with the rich TensorFlow ecosystem. As described above, you can use Tensorboard to visualize ranking metrics like NDCG and MRR, as well as to pick the best model checkpoints using these metrics. Once your model is ready, it is easy to deploy it in production using TensorFlow Serving.
If you’re interested in trying TF-Ranking for yourself, please check out our GitHub repo, and walk through the tutorial examples. TF-Ranking is an active research project, and we welcome your feedback and contributions. We are excited to see how TF-Ranking can help the information retrieval and machine learning research communities.
This project was only possible thanks to the members of the core TF-Ranking team: Rama Pasumarthi, Cheng Li, Sebastian Bruch, Nadav Golbandi, Stephan Wolf, Jan Pfeifer, Rohan Anil, Marc Najork, Patrick McGregor and Clemens Mewald. We thank the members of the TensorFlow team for their advice and support: Alexandre Passos, Mustafa Ispir, Karmel Allison, Martin Wicke, and others. Finally, we extend our special thanks to our collaborators, interns and early adopters: Suming Chen, Zhen Qin, Chirag Sethi, Maryam Karimzadehgan, Makoto Uchida, Yan Zhu, Qingyao Ai, Brandon Tran, Donald Metzler, Mike Colagrosso, and many others at Google who helped in evaluating and testing the early versions of TF-Ranking.
Pre-order Asmus Toys LOTR021 The Lord of the Rings Series: Arwen 1/6 Figure from KGHobby (link HERE)
“If you want him, come and claim him!”
Sideshow and Asmus Toys are proud to present the Arwen Sixth Scale Figure based on the likeness of Liv Tyler from the film.
Arwen was the youngest child of Elrond and Celebrían. Her elder brothers were the twins Elladan and Elrohir. Her name, Ar-wen, means ‘noble maiden’. She bore the sobriquet “Evenstar” (or Evening Star), as the most beautiful of the last generation of High Elves in Middle-earth. Arwen is half-Elven. In marrying Aragorn after the War of the Ring, she became Queen of the Reunited Kingdom of Arnor and Gondor. Arwen helped unite Elf and Man in peaceful love and harmony, in the process of becoming mortal.
The daughter of Elrond comes with a detail design dark blue robe with silk underlining and adorn with corset drawstring tied by the metal medallion buckle. The hairstyle is part sculpted, part rooted, both having the braidings as worn by actress Liv Tyler in the film.
The body is equipped with parted boots and strong ankle joint for better posing possibilities. The signature weapon Hadhafang used to protect Frodo is made in diecast for a more realistic look.
Asmus Collectible Toys 1/6th scale Arwen 12-inch Collectible Figure features: Authentic and detailed fully realistic likeness of actress Liv Tyler as Arwen from The Lord of the Ring trilogy | Approximately 28 cm tall Asmus Toys female body with over 36 points of articulation |Four (4) interchangeable gloved hands. Clothing: dark blue robe with drawstring and metal buckle, dark blue long underpants, two parted boots (enhanced ankle joints), glove arm wrap. Weapons: die-cast Hadhafang, sheath
Accessories: Asmus Toys figure stand