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OMEGA MODERN includes over one hundred character sprites, and that isn't even counting the animals, aliens or robots. Each human character also has a face graphic, and sixteen of them have fully animated side-view battlers so they can be used as heroes. Each monster has an overworld walking sprite as well as a image to be used in side-view combat. On top of that, extra character sets are included for cars, vehicles, treasure chests, switches, and special effects.
Twenty-six tilesets– including autotiled water, terrain and buildings– provide a variety of options for creative locations. Forests, caves, towns and cities are a given– there's also tiles for a school, a science lab, a fast-food restaraunt, bathrooms, and more. House and building tiles can be mix-and-matched for even more options in creating your game world. Also included: a huge set of icons for items, weapons, equipment, etc.
26 tilesets! Interiors and exteriors– towns, cities, forests, dungeons!


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text:
by Francesca Dunn
18 February 2016, 10:40pm
sunflower bean: new poster kids of rock 'n' roll
Ahead of their show in London tonight, we meet the band taking over the world one powerful performance at a time.
As they soundcheck amongst the old-school social club charm of gold tinsel streamers and velvet booths at East London's Moth Club, Julia Cumming, Nick Kilven and Jacob Faber already have i-D's undying love. The Brooklyn band run on a sort of magic formula, with Nick and Julia's some-time harmonies, some-time call and response vocals entwined in a fiery relationship as she thrashes her bass and he plays the guitar better than anyone we know. As Jacob and his drums keep their sexy psychedelic sound under control, it's increasingly difficult to believe/remember/accept that all three of them are only 20 years old. Having spent much of the past year on the road with DIIV, The Vaccines and label mates Fat White Family, it's a wonder they found time to both record and release their brilliant debut album, Human Ceremony. Out now on Fat Possum, the LP marks their first time working with a producer - they recorded it in just 11 days with Matt Molnar - and though the result is just as awesome as their Show Me Your Seven Secrets EP, you can hear their growth in the record's polished sound. They wrote it, as they always have done, in the basement of Jacob's parents' house. The sound down there is really good… plus, well, it's their special place. Lou Reed once said that you make an album and then you have the rest of your life, and the band regularly looks to this quote for motivation. "I think developmentally, it's good to draw a line under something, to stand by it and say: this is our first record… and it's not the only record we're gonna make," Julia attests. "I think it's different now than it once was. In the digital age everything you put out lives forever. Everything's accessible. It's not like how David Bowie could just go through phases and then bury them."
"I was thinking about when you look through a dollar bin in a record store," Nick continues, "and you see things by artists who were good enough to make a record and be on a label, but they've been forgotten for whatever reason. I bet they were actually really cool and popular and probably friends with all our favourite bands." He has a point. We should start rescuing the forgotten artists. That said it looks unlikely that Sunflower Bean will suffer the same fate. Finding herself muse to Hedi Slimane, Julia has a series of Saint Laurent campaigns under her belt and appears on billboards for the house across the world. "I started keeping a diary so that I can remember all the amazing things that are happening. Not that it's all peaches and cream, but… you know. We're livin' some kind of dream." So now their album is out, what are they going do with the rest of their lives? "You mean when we're old and washed up?" Not exactly Nick, but tell us anyway. "Well, I love collecting things, so I just wanna have a house that we can all live in together. I want ten pianos that I'm gonna find on Craigslist and I'll put them all in the same room. I'll also have a clock room, a mirror room, a flower room…" And until Sunflower Bean curate this mad house? "We'll just be making records…" says Julia, "and hopefully they won't end up in the dollar bin!"
Text Francesca Dunn
Photography Eleanor Hardwick
Julia wears jacket Saint Laurent by Hedi Slimane. All other clothing model's own.
Sunflower bean
The Fashion Issue
francesca dunn

redpajama_set_name: RedPajamaCommonCrawl


text:
MoMA Learning
Browse by Artists
glossary of art terms
Migration and Movement
Artists move around the world, shifting their identities, cultural traditions, and artistic techniques.
MoMA-Learning_Worksheet_MigrantStories
"Migration and Movement" Powerpoint Slideshow (.ppt)
"Home Dictionary" Worksheet (.doc)
"Make A Family Immigration Map" Worksheet (.doc)
"On The Move" Worksheet (.doc)
Tools & Tips:
People have always moved around the world. Early humans were nomadic, traveling in search of food, shelter, and safety. Today, people move for many different reasons, including economic, political, cultural, religious, and environmental. Sometimes, events beyond people's control, like war or natural disaster, leave them displaced and forced to migrate. Other times, people migrate voluntarily, perhaps in search of better work opportunities or a different lifestyle. For many artists, their own migrations and those of their ancestors shape their identities and the art they produce.
As people move, they bring their traditions, knowledge, and beliefs with them. Often, as much as they absorb the culture of their new home, they influence it with their own traditions.
To explore more, click on each artwork thumbnail, then click again on the larger image that appears in the box above.
An image, especially a positive print, recorded by exposing a photosensitive surface to light, especially in a camera.
A brief, evocative description, account, or scene.
A representation of a person or thing in a work of art.
The customs, arts, social institutions, and achievements of a particular nation, people, or other social group.
A setting for or a part of a story or narrative.
A work of art made with a pencil, pen, crayon, charcoal, or other implements, often consisting of lines and marks (noun); the act of producing a picture with pencil, pen, crayon, charcoal, or other implements (verb, gerund).
An element or substance out of which something can be made or composed.
Derived from the French verb coller, meaning "to glue," collage refers to both the technique and the resulting work of art in which fragments of paper and other materials are arranged and glued or otherwise affixed to a supporting surface.
A term generally used to describe art that is not representational or based on external reality or nature.
Related Artists: El Anatsui, Yto Barrada, Mona Hatoum, Yun-Fei Ji, Jacob Lawrence, Mateo López, Zarina
Questions & Activities
Why do people migrate? Brainstorm a list of reasons why people move from one place to another.
Look at the works of art in this theme. How would you describe the types of migrations these artists portray? In discussion with a partner, compare and contrast the ways these artists portray migration.
Migrant Stories
Jacob Lawrence's Migration Series (1940–41) tells the story of African Americans moving from the southern United States to the North.
Interview someone who migrated from one place to another. It could be someone who immigrated to a new country or someone who moved from one town to the next town. Gather background information about why they left and how they chose their new home.
Tell your interviewee's story. Illustrate scenes from their journey by drawing a series of images, just as Jacob Lawrence did, or by writing several one-paragraph vignettes.
Home Dictionary
What does home mean to you? For Zarina, the images in Home Is a Foreign Place (1999) serve as a visual vocabulary of things related to home. She created abstract renderings of words like "afternoon," "hot-breeze," and "threshold."
Make a list of words that remind you of your home (or homes). They can be nouns, adjectives, and verbs; they can refer to tangible objects or abstract ideas.
In a notebook, write one word from your list at the bottom of each page. This notebook will be your home dictionary.
Over the next month, fill in the pages of the notebook with images that illustrate these words. Collect photographs, ticket stubs, ribbons, images and words from magazines, and other materials that represent your words, and collage them into your notebook.
Make a Family-Immigration Map
Where in the world has your family been? Print out a copy of the map of the world.
Identify the different places where you have lived and mark them on the map.
Next, research immigration in your family history. Interview relatives about how your ancestors traveled, both within your country and across continents. Mark your family's movement on the map.
Share your map with a partner. How was your family's journey different from that of your partner's family?
Investigating Identity
Maps, Borders, and Networks
WEBSITE: Revisit MoMA's One-Way Ticket: Jacob Lawrence's Migration Series exhibition
AUDIO: Hear about some of the projects highlighted in MoMA's Insecurities: Tracing Displacement and Shelter exhibition
WEBSITE: Discover millennia of human migration on National Geographic's Map of Human Migration
BLOG POST: Hear from MoMA teaching artists Shellyne Rodriguez and Kerry Downey about their experience teaching with Migration Series
BLOG POST: How Will Art Solve Problems?
TOOLS & TIPS / ART TERMS / ABOUT


The Hawth's amphitheatre provided the perfect setting for Illryria's latest production.
The theatre company's latest show is Dr Doolittle and again they put on a colourful, musical and impressive production.
The cast of six, led by Edward Simpson as the titular doctor, entertained the crowds with wonderful songs, great comic timing and hugely impressive puppetry.
The story tells of how Doctor John Dolittle learns how to talk to animals and news of his skills spreads far and wide. Soon animals across the world send messages asking for his help. So, he buys an old ship and, together with his trusty pet-crew go on a mission to heal illness, spread compassion and gain a greater understanding of the animal kingdom.
It's an exciting adventure with a strong message about responsibility towards animals and the environment.
The puppets are what always impress me about Illyria, and this year was no exception. There was everything from cats and dogs to horses and sharks. In fact the shark was very inventive and a highlight.
The songs were performed with gusto, especially from Paula Brett, and at times extremely humorous, with the pig (Callum Stewart) singing about cake a particular favourite.
The balmy evening made it a perfect night for watching theatre outdoors and it's great Illyria and returning to the Hawth's amphitheatre with The Merchant of Venice (Friday August 10) and The Hound of the Baskervilles (Friday August 31).
Visit the Hawth's website for more details.


The object of the game is to build a line of four of your marbles while trying to stop your opponent (human or computer) building a line of his or her own. A line can be horizontal, vertical or diagonal.
3 years ago 5 super!!!
5 years ago 5 great game!


text:
Surname: Brownell
Read Genealogy of Newport RI and Freetown MA
The Read family of which the late Paddock Richmond Read was a descendant, is one of the oldest and best known families of this section of New England. The first of the name in New England wasJohn Reade, a cordwainer by trade, according to tradition, as is his coming from Plymouth, England. He came to this country and was an inhabitant of Newport, R. I.
Ancestry of the Jennings Family from Fall River, Massachusetts
Several persons bearing the name Jennings (variously spelled) located in Massachusetts in its early settlement. Richard Jennings put himself as apprentice to Robert Bartlett, of Plymouth, in 1635, for a period of years. He is said to have lived at Sandwich, whence he moved to Bridgewater, and had a family of children. The Jennings family was long prominent and highly respected in the town of Sandwich, but in time became practically extinct there. Thomas Jennings was an early settler in Portsmouth, R. I. It is, however, the purpose to refer here to the special Fall River family of the name the head of which was the late William H. Jennings. The latter was a descendant in the seventh generation from John Jennings of Sandwich, Mass., from whom his descent is through Isaac, John, Isaac, Isaac and Andrew M. Jennings. These generations follow in the order named.
Allen Genealogy of New Bedford Massachusetts
The Allen family of New Bedford Massachusetts is descended from George Allen who's children and descendants would eventually settle in Old Dartmouth and New Bedford Massachusetts. This is an extensive genealogy of five sons of the fourteen children of James Allen and Sarah Howland of New Bedford MA, including the ancestry of James Allen back to George Allen.
Hawes Family of Wrentham, MA
For generations, since the early Colonial period, the Hawes family has been resident in Wrentham, Mass. The line is traced back to Edward Hawes, of Dedham, Mass., born probably about 1620, who died in 1686. He married April 15, 1648, Eliony Lombard. This genealogy discusses the line from Edward through Oliver Snow Hawes who removed to Fall River Mass. It then discusses the family and descendants of Olvier Snow Hawes who resided in the vicinity of Fall River.
Ancestors of George Albert Chace of Fall River, MA
CHACE (Fall River family). In and about Fall River for generations the Chace (earlier generations using the orthography Chase) family has been conspicuous in the affairs of that section and especially for a century past in its industrial life. In the particular line of Joseph Chase, who settled in Swansea, the family becoming a Swansea-Somerset one, such names as Oliver, Harvey, James H., Hon. Jonathan and George Albert Chace – the latter of whom planned and built a number of the large cotton mills of Fall River, was long treasurer and manager of the Bourne Mill, in Tiverton, R. I., and as well was a most active and useful citizen of Fall River – will long endure in the annals of this great industrial section. There follows from the first American ancestor of the Chaces named to the present in chronological order the genealogy and family history of the children of the late George Albert Chace of Fall River.
Ashley Family of New Bedford, MA
ASHLEY (New Bedford family). Among the first settlers of Rochester, Mass., and their families appear the names of Joseph Ashley and his wife Elizabeth and their children. There had settled at Springfield as early as 1639 Robert Ashley; and from the fact that many of the early settlers of Springfield were drawn from Roxbury by Pynchon, perhaps Mr. Ashley had been there previously a short time. One Thomas Ashley resided at Cape Ann (Gloucester) in 1639; he was admitted an inhabitant of Boston in 1658, and was probably the Thomas Ashley of Maine, 1654, who, says Savage, may have removed
1918 Warren County Farmers' Directory – B Surnames
Abbreviations Used in this Directory a–Acres; Ch — Children; O–Owner; T–Tenant or Renter; R –Rural Route; Sec-Section; Maiden name of wife follows directory name in parentheses (); figures at end of information–year became resident of county. Star (*) indicates children not at home. Name of farm follows names of children in quotations marks. In case of a tenant, the farm owner's name follows the figures giving size of farm. Example: ABBEY, William L. (Lena Riggs) Martha and Cora Abbey, Mother and Sister; Kirkwood R1 Tompking Sec8-5 T80a H.M. Abbey Est. (1886) Tel. Farmers' Line Kirkwood MEANS ABBEY, William L. –
Seabury Family of New Bedford, Massachusetts
SEABURY – variously spelled Sebury, Saberry, Saberrey and Sabury. The American ancestor of the Seaburys of New Bedford was (I) John Seabury, of Boston, who died before 1662. He married Grace, and had two sons – John (who went to Barbados) and Samuel (born Dec. 10, 1640) – and several daughters. (II) Samuel Seabury, son of John, born Dec. 10, 1640, died Aug. 5, 1681. He married at Weymouth Nov. 9, 1660, Patience Kemp, who died Oct. 29, 1676. He married (second) April 4, 1677, Martha Pabodie, daughter of William and Elizabeth (Alden) Pabodie and granddaughter of John and Priscilla
Contributions of the Lowell Massachusetts Historical Society
The Lowell Historical Society of Lowell Massachusetts published 2 volumes of "contributions" to the recording of the history of Lowell Massachusetts at the turn of the century. These contributions were preceded by the contributions by the Old Residents Historical Association of Lowell, Massachusetts. Table of Contents Volume I Bunker Hill, The Battle of, and Those Who Participated Therein from the Towns from which Lowell was Formed, Mrs. Sara Swan Griffin Fiske, Rev. John, by Henry S. Perham Francis, Mrs. Sarah W., by Miss Mabel Hill Lincoln, Abraham, Centennial Anniversary of Introductory Address, Solon W. Stevens Recollections of Lincoln in Lowell


The Canon 80D is a beauty, and what makes it even more awesome is that there's a tonne of accessories in the market that can help you maximize its capabilities in order to take way better shots. This awesome camera combined with these accessories, there's no telling how great the photos will look.
So what accessories should you look for when buying the Canon 80D bundle?
A tripod is a very essential accessory when it comes to the Canon 80D camera. Why is this, you ask? Well, it is because a good tripod will make it easy for you to take photos in almost all circumstances. It will give you more flexibility when shooting photos.
The importance of a good flash cannot be stressed enough when it comes to the art of photography. A good flash guarantees good photos, so make sure you invest in a good flash when purchasing a Canon 80D. A perfect example of a good flash is the Speedlite flash.
Nothing is as annoying as missing a great shot because you have a slow memory card. It's the absolute worst! That is why you need a high-speed memory card. One that writes data really fast for example at 90MB/s. This is a very vital accessory that you should look out for when buying the Canon 80D bundle. No more missing great moments!
Imagine walking around with this powerful piece of art without a camera bag. Having a bag ensures that your camera, together with its accessories, is secure. A good camera bag will increase your camera's durability since it helps prevent minor damages to your camera and its accessories. Ensure your bag has ample storage space and also a lot of padding.
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There's no denying that the Canon 80D is an amazing camera. Combine it with its accessories and the sky is the limit when it comes to what your camera can do.


text:
\section{Introduction}
Image-to-Image Translation (IIT) aims to learn the mapping from a source image to a target image. Unlike traditional style-transfer methods~\cite{DBLP:journals/corr/GatysEB15a}, recent methods leverage Generative Adversarial Networks for learning the end-to-end mapping, e.g. \cite{isola2017image}. Supported by the adversarial loss, the discriminator can learn a similarity measure optimized for a specific task rather than using a hand-engineering one, which enables the method to easily adapt to multiple tasks, such as translating color images to edge maps, grayscale images to color images, and labels to street scenes.

Previous IIT methods can be categorized into two broad classes, including \emph{methods that learn from paired training data} and \emph{methods that learn from grouped training data}. The first class of methods that learn from paired data require that each source image in a training set is explicitly associated with a corresponding target image. To collect such training data requires non-trivial labelling efforts, in particular when the data set is sizable. To alleviate this burden in gathering paired training data, the second class of methods that learn from grouped training data are introduced. These methods permit a training dataset to be organized in a way where a group of source images are associated with another group of target images without the need to specify the one-to-one correspondence between the two groups of images, which nevertheless needs human efforts in organizing training data~(Left in Fig.~\ref{fig:domain}).

Another limitation of existing image translation approaches is their degraded performance when training data from multiple groups are not balanced. For instance, the age attribute in CelebA~\cite{liu2015deep} is not balanced, where images of younger people are much more than images of older individuals. In terms of unbalanced attributes, the majority group is referred to as MA; while the minority group is referred to as MI. Most of the previous works will suffer this problem~\cite{choi2017stargan, larsen2015autoencoding}. To overcome the problem, a method called ResidualGAN~\cite{shen2017learning} is introduced, which undersamples the MA group to achieve the balance between MA and MI groups before the training step. This undersampling process can easily lose valuable information, whose performance can become particularly problematic when the minority group has a very small size.

\begin{figure}[t]
\vspace{-0.4cm}
\begin{center}
\includegraphics[width=1.0\linewidth]{figure_domain2.pdf}
\end{center}
\vspace{-0.4cm}
\caption{A sparsely grouped dataset consists of a most portion of mixed data and a few quantity of group-labelled data. Here, grouped training dataset consists of two groups, one for black hair with $c=0$ and another for blond hair with $c=1$.}
\label{fig:domain}
\vspace{-0.5cm}
\end{figure}

To alleviate these problems, we propose a novel sparsely grouped learning method, where only a few of the training dataset is grouped while the remaining unlabelled data~(i.e. mixed data) as shown in Fig.~\ref{fig:domain} is used for unsupervised learning to improve the performance of classification and stabilize the training of the adversarial network. Therefore, our method is actually semi-supervised learning. The sparsely grouped learning will work well for unbalanced data in IIT task, as unused data can be fully utilized for unsupervised learning after following up undersampling procedure to balance MA group and MI group. To the best of our knowledge, no previous image translation architecture can be directly applied to learn from sparsely grouped datasets as an off-the-shelf tool. To address the gap, we propose a one-input multiple-output network, called \emph{Sparsely Grouped Generative Adversarial Networks(SG-GAN)}, for learning to translate a diverse set of image attributes among multiple groups.

Overall, the main contributions include:
\begin{itemize}
\item We propose SG-GAN, a novel generative adversarial network for tackling image-to-image translation tasks by learning mapping among multiple groups in a sparsely grouped dataset where only a few portion of data points are associated with their group-labels while the group affiliation for the most of data points remains unknown.
\item The proposed SG-GAN can generate comparable facial attribute translation results using much fewer group-labelled samples than peer methods; SG-GAN also outperforms state-of-the-art methods for facial attribute manipulation working with severely unbalanced dataset.
\item We further introduce an adapted residual image learning component into the proposed SG-GAN to improve the degree of translation for the targeted image attribute involved in a translation process while preserving other visual attributes unrelated to the translational goal in the generation results.
\end{itemize}
\section{Related Work}

\begin{figure*}[t]
\vspace{-0.4cm}
\begin{center}
\includegraphics[width=1.0\linewidth]{figure_model_2.pdf}
\end{center}
\vspace{-0.4cm}
\caption{The multi-task learning architecture of SG-GAN. $G$ is a one-input, multi-output network that performs multiple attributes manipulation(e.g., gender(male/female), hair color(black/blond)). Adapted residual image learning just concatenates input $x$ into the middle feature maps of decode network as the input of the next convolution layer. Note that $D$ will be a semi-supervised classifier when trained in the sparsely grouped dataset.}
\label{fig:model}
\vspace{-0.5cm}
\end{figure*}

{\bfseries Generative Adversarial Networks:} In addition to the variational autoencoder(VAE)~\cite{DBLP:journals/corr/KingmaW13} and PixelCNN~\cite{van2016conditional}, Generative Adversarial Networks(GAN)~\cite{goodfellow2014generative} provides a more powerful framework for generating sharp and realistic images~\cite{DBLP:journals/corr/RadfordMC15, Zhang_2017_ICCV, nguyen2017plug}. Recently, \cite{karras2017progressive} proposed a new training methodology for GAN to progressively train the generator and discriminator. Their method can generate highly realistic facial images of $1024^{2}$ pixels. With its rapid development and optimization, GAN has been applied to many fields, for example, image in-painting~\cite{pathak2016context, IizukaSIGGRAPH2017}, image super-resolution~\cite{Ledig_2017_CVPR}, style transfer~\cite{li2016precomputed, Zhao:2017:SAA:3123266.3123450}, video prediction~\cite{mathieu2015deep, Pan:2017:CYT:3123266.3127905, liu2018attentive} and object detection~\cite{Wang_2017_CVPR}.

Researchers have developed a rich collection of techniques to improve both the training stability and diversity of images generated by GAN. To attain more stable training, objective functions of GAN are carefully designed. For example, LS-GAN~\cite{Mao_2017_ICCV} adopts a least squared loss function in its discriminator to solve the vanishing gradient problem. The Wasserstein GAN(WGAN) proposed in \cite{pmlr-v70-arjovsky17a} uses the Wasserstein distance instead of the Jensen-Shannon distance to form its objective function, which achieves a more stable training process. For the latter aim, \cite{salimans2016improved} proposed a method to facilitate the convergence of a GAN by adopting virtual batch normalization to replace batch normalization~\cite{ioffe2015batch}.

{\bfseries Image-to-Image translation(IIT):}
The essence of IIT is to learn the mapping between pairs or groups of images while preserving image characteristics irrelevant to the current translation task. The prior work of IIT using conditional GAN \cite{isola2017image} has attained impressive results. Their method applies a supervised learning-based approach onto pairs of IIT images in the training phase, which incurs a major limitation for large-scale application and migration into new domains and tasks. To overcome the aforementioned limitation, a collection of IIT methods based on learning from group-labelled training data was proposed, e.g., \cite{Yin:2017:LGE:3123266.3123423, liu2017unsupervised,dong2017unsupervised,Zhu_2017_ICCV, pmlr-v70-kim17a, shen2017learning, DBLP:journals/corr/abs-1709-06548, Liu:2017:FAC:3123266.3123431}, to reduce the ground truth group-label acquisition efforts for training data preparation.

In addition to directly learning the mapping from one or multiple source images to one or multiple corresponding target images, another thread of active research endeavors is to conduct disentangled representation learning, the result of which can then be leveraged for facial attribution manipulation in images~\cite{larsen2015autoencoding, DBLP:journals/corr/DumoulinBPLAMC16, brock2016neural, NeuralFace2017, xiao2018elegant}. Recently, StarGAN~\cite{choi2017stargan} is proposed, which utilizes a GAN-based architecture to learn a series of mappings from a common group of source images to multiple groups of target images using a single generative model. In comparison, the new SG-GAN algorithm proposed in this paper requires a much smaller amount of ground truth group-labels in its learning process, thanks to its semi-supervised learning framework.

{\bfseries Facial Attribute Manipulation:}
Facial attribute manipulation is a special IIT task, which aims at modifying the semantic content of a facial image according to a specified attribute value. \cite{larsen2015autoencoding} proposed a novel model, called VAE/GAN, for facial attribute manipulation but acquires labelled data to compute the visual attribute vectors after training. Another model called Adversarially Learned Inference \cite{DBLP:journals/corr/DumoulinBPLAMC16} is based on conditional GAN, which must be embedded with binary attributes when trained for the image semantic translation task.~\cite{pmlr-v77-zhang17c} proposed a model called ST-GAN, which can be trained on a mixed dataset to establish relationships between latent codes and generated samples for semantic information discovery. Even though the design approach of their method is inspiring, the quality of its IIT result yet needs to be further improved.

Recently, GAN-based residual image learning has been applied to facial attribute manipulation, the method of which is referred to as ResidualGAN~\cite{shen2017learning}. ResidualGAN attains satisfactory IIT results. In comparison, the proposed SG-GAN can obtain multiple facial attribute manipulation effects using only a single trained model, which also produces more visually realistic IIT results.

{\bfseries Semi-Supervised Learning using GAN:}
A substantial amount of efforts have been dedicated to conducting semi-supervised learning using GAN, e.g., \cite{salimans2016improved, NIPS2017_7229, DBLP:journals/corr/Springenberg15, DBLP:conf/acl/YangHSC17, 2016arXiv160507725M}. For example, the method introduced in \cite{NIPS2017_7229} improves the performance of semi-supervised image classification in several benchmark datasets. For these semi-supervised GAN models, discriminator $D$ will receive three different sources of data in its training process: real labelled images for supervised learning, real unlabelled images and fake images for unsupervised learning.

\section{Methods}

\subsection{Generative Adversarial Networks}

Goodfellow~\cite{goodfellow2014generative} proposes a GAN that consists of one generative model $G$ and another discriminator model $D$. Its training process can be treated as a minimax game in which $D$ learns to distinguish between real samples and generated samples and at the same time $G$ tries to learn to generate samples $G(z)$ from the random noise $z$ to match the distribution of real samples $x$ and fool $D$. The objective function of GAN is given as follows:
\begin{eqnarray}
\mathop{min}\limits_{G}\mathop{max}\limits_{D}\ell(D,G) = \mathbb{E}_{x}[logD(x)]
+ \mathbb{E}_{z}[log(1-D(G(z)))]
\end{eqnarray}

\subsection{SG-GAN}

{\bfseries One-Input Multiple-Output Architecture for Multi-task Learning:}
Unlike the vanilla GAN model~\cite{goodfellow2014generative}, which directly learns the mapping from a noise vector $z$ to images $x$, the $G$ in IIT task learns the mapping from an input image $x$ to an output image $\tilde x$, which can be regarded as an ``autoencoder"~\cite{article}.

The previous methods have two corresponding generator networks with input images from different groups ~\cite{choi2017stargan,Zhu_2017_ICCV}. However, such architecture with two generators can not work well in the sparsely grouped dataset where most data are mixed while only a few of data is group-labelled. It is hard to learn a good mapping from a source group to a target group using few group-labelled samples. Recently, StarGAN~\cite{choi2017stargan} uses a single generator network for manipulating multiple attributes. It is difficult to train their model on a sparsely grouped dataset because of the lack of original domain labels to assess the reconstruction loss.

As illustrated in Fig.~\ref{fig:model}, we propose a one-input multi-output architecture as generator $G$, which can use an unlablled data point $x$ as the input and maps it to all existing groups. It is obvious that both labelled and unlabelled images can be used as input to $G$. The discriminator $D$ is a multi-task learning network, which performs adversarial-learning and classified-learning with multiple facial attributes. The final output of $G$ is $\tilde x^i_{j}, 0 \leq i < m, 0 \leq j < n$, where $m$ is the range of every attribute value and $n$ is the number of facial attributes. Except for adversarial learning, the $D$ network can be regarded as multiple classifiers with outputting $n$ logit vectors where every vector with $m$-dim is used as input to a softmax function. The output dimensions of $G$ and $D$ will increase as the number of facial attributes participating in the manipulation grows.

\begin{figure}[t]
\vspace{-0.5cm}
\begin{center}
\includegraphics[width=1.0\linewidth]{figure_recon_loss.pdf}
\end{center}
\vspace{-0.4cm}
\caption{The reconstruction learning process in SG-GAN ($m=2$).}
\label{fig:recon_loss}
\vspace{-0.5cm}
\end{figure}

\begin{figure*}[t]
\vspace{-0.6cm}
\begin{center}
\includegraphics[width=1.0\linewidth]{figure_com2.pdf}
\end{center}
\vspace{-0.4cm}
\caption{Facial attribute translation results on CelebA test dataset. The first and second rows show facial attributes manipulation results of the baseline methods, i.e. ResidualGAN and StarGAN; the third row shows results of the proposed model in the condition that all data is grouped; the results are shown in the last two rows when using 5000 and 500 images for every value of the attribute as the grouped data, respectively. G: gender; S: smile; H: hair color.}
\label{fig:comparison}
\vspace{-0.5cm}
\end{figure*}

{\bfseries Sparsely Grouped Learning:} We construct a discriminative network for classification and to distinguish generated samples $\tilde x$ and real samples $x$. The $D$ for classified-learning need to classify input samples by the attribute value and build the objective function for every attribute.
Given the real image $x$ with the original group $c_{j}$ for the $j$-th facial attribute. The objective function of $D$ for this attribute is the softmax loss:
\begin{eqnarray}
\ell_{d\_cls} = \mathbb{E}_{x, c_{j}}[- \log(D(c_{j} | x)],
\end{eqnarray}
where $D(c_{j} | x)$ is the softmax probability over this group-label $c_{j}$.
In the same way, the generated samples $\tilde x$ with the target group $\tilde c_{j}$. The softmax objective function for training $G$ is:
\begin{eqnarray}
\ell_{g\_cls} = \mathbb{E}_{\tilde x, \tilde c_{j}}[- \log(D(\tilde c_{j} | \tilde x)]
\end{eqnarray}

Different from the previous GAN model, SG-GAN would output multiple generated samples $\tilde x^{i}_{j}$, $i=0,1,...,m-1$ for the $j$-th facial attribute. The min-max adversarial loss for generator $G$ and discriminator $D$ with this facial attribute is:
\begin{eqnarray} \label{eq_1}
\ell_{adv} &=& \mathbb{E}_{x}[logD(x)]  \nonumber \\ &+& \sum_{i=0}^{m-1}[\mathbb{E}_{\tilde x^i_{j}}[log(1-D(\tilde x^i_{j}))]]
\end{eqnarray}

Noted that the $D$ trained with grouped data carries both loss terms $\ell_{d\_cls}$ and $\ell_{adv}$ in its objective function, while the network trained with mixed data only computes a single loss term $\ell_{adv}$ in its objective function

{\bfseries Adapted Residual Image Learning:} The residual image learning that was proposed by~\cite{shen2017learning} aims to improve the effectiveness of facial attribute manipulation and make the modest modification of the attribute-specific facial area while keeping irrelevant content unchanged. However, this model with the residual image learning, which sums the natural images and outputs of network linearly is hard to generate very realistic images, especially based on the vanilla GAN loss~\cite{goodfellow2014generative}. Through experiments, we found that learned residual images tend to be sparse when using very powerful GAN loss terms~\cite{gulrajani2017improved}.

To alleviate these problems, we improve the previous residual image learning method from two aspects. One is to use the concatenation of the feature maps and input $x$ instead of their sums, the other is that we add the new convolution layer to refine the input as the final translation result. As shown in Fig.~\ref{fig:model}, we name this novel method as adapted residual image learning and adapt it to our architecture.

{\bfseries Reconstruction Loss:}
By minimizing the adversarial and classification losses, $G$ is trained to generate images that are realistic and classified into the correct target group.
However, minimizing these losses does not guarantee that translated images preserve the identity and background content of the input image. To alleviate this problem, the previous methods apply a cycle consistency loss~\cite{Zhu_2017_ICCV, choi2017stargan} to the generator. This loss does not adapt to our architecture. As shown in Fig.~\ref{fig:recon_loss}, we propose a reconstruction loss for the generator, defined as:
\begin{eqnarray} \label{recon_loss}
\ell_{rec} &=& \Vert G(G(x)^0)^1 - G(x)^1 \Vert_{1} \nonumber \\ &+& \Vert G(G(x)^1)^0 - G(x)^0 \Vert_{1},
\end{eqnarray}
where $G(x)^i$ means the $i$-th output of the $G$. $G$ uses $x$ as input to obtain the translation result $\tilde x^{0}$ and $\tilde x^{1}$. Then, these results will also be new input to obtain new translation result $\hat x^{1}$ and $\hat x^{0}$ of corresponding groups. We adapt this $L_1$ norm as the loss to make $\tilde x^{0}$ and $\hat x^{0}$, $\tilde x^{1}$ and $\hat x^{1}$ as close as possible. Besides keeping the content of images fixed for image translation among different groups, our reconstruction loss could also keep the content fixed for images translation between the same group.

{\bfseries Overall Objective Function:}
The objective functions will be different for the mixed data and grouped data, as $\ell_{d\_cls}$ is just for the grouped data with group-labels. Finally, the full objective functions for the specific facial attribute to optimize $D$ is shown as:
\begin{eqnarray}
\ell_{D} =
\begin{cases}
\ell_{d\_cls} - \ell_{adv} &\text{Grouped},\\

- \ell_{adv} &

\text{Mixed}.
\end{cases}
\end{eqnarray}

For $G$, the objective function with this facial attribute is:
\begin{eqnarray}
\ell_{G} = \ell_{g\_cls} + \ell_{adv} + \alpha \ell_{rec},
\end{eqnarray}
where $\alpha$ is a hyper-parameter for reconstruction loss. We use $\alpha = 10$ in all our experiments.

\section{Implementation}

\begin{figure*}[t]
\vspace{-0.4cm}
\begin{center}
\includegraphics[width=1.0\linewidth]{figure_com_unb.pdf}
\end{center}
\vspace{-0.4cm}
\caption{The translation results of the unbalanced facial attribute on CelebA test dataset. Three rows show results using different models: ResidualGAN, StarGAN and SG-GAN, respectively. And the first column shows input images while the next columns are generated using the different number of the MI group for pale skin. Results of the dual task for the same attribute manipulation are shown in the right.}
\label{fig:comparison_unb}
\vspace{-0.4cm}
\end{figure*}

{\bfseries Network Architecture:} Our architecture is similar to previous IIT methods, which have shown impressive results for style transfer and semantic manipulation. Our generator $G$ contains convolution layers with the stride size of two for encoding, some residual blocks for expanding the receptive field, transposed convolution layers for decoding. We use instance normalization~\cite{articlein} for the generator but no normalization for the discriminator. Note that $tanh$ activation function is used for output of the generator. More details about network architecture are shown in the Appendix~\ref{net_arch}.

{\bfseries Training Details:}
We apply new technique from Wasserstein GAN with gradient penalty~(WGAN-GP)~\cite{gulrajani2017improved} to stabilize training process and generate high quality images. We replace Eq.~\ref{eq_1} with the new object function of WGAN-GP defined as:
\begin{eqnarray}
\ell_{adv} = \mathbb{E}_{x}[D(x)] - \sum_{i=0}^{m-1}(\mathbb{E}_{\tilde x^i_{j}}[D(\tilde x^i_{j})] - \lambda \mathbb E(t^{i}_{j})),
\end{eqnarray}
where $t^{i}_{j}$ is a gradient penalty variable for training $D$ and more details can be found in~\cite{gulrajani2017improved}. $\lambda$ is 10 for all our experiments.

We use the Adam~\cite{DBLP:journals/corr/KingmaB14} with $\beta_{1}=0.5$ and $\beta_{2}=0.999$. The size of the training batch is set to 8 for our experiments. Similar to WGAN-GP~\cite{gulrajani2017improved}, the generator is updated once after every five updates performed over the discriminator. Our all models are trained with the learning rate of 0.0001 for the first 10000 iterations and the learning rate will be linearly decayed to 0 over the next 10000 iterations.

{\bfseries Coping with Unbalanced Dataset:} SG-GAN starts with an undersampling procedure to balance the majority and minority groups. However, rather than discarding data through an undersampling process by existing methods, the proposed method uses a follow-up semi-supervised learning procedure where unsampled data elements are observed as unlabelled records during a learning process. In this way, all dataset elements are effectively utilized for model training, without overlooking informational clues carried by any one of them.

\section{Experiments}

We examine our proposed model, SG-GAN, on a sparsely grouped face dataset. In this section, we firstly compare SG-GAN against recent methods on facial attribute manipulation tasks via both qualitative and quantitative evaluation. Next, we analyze the problems of image translation for previous works, demonstrate results that SG-GAN can achieve more realistic and apparent translation results in the unbalanced data. Lastly, we conduct an ablation study and compare the proposed method against several reduced variants to show the effectiveness of the adapted residual image learning component in the method.
\subsection{Baseline Models}

For multiple facial attributes manipulation, StarGAN~\cite{choi2017stargan} has achieved state-of-the-art results compared with DIAT~\cite{DBLP:journals/corr/LiZZ16e}, ICGAN~\cite{Perarnau2016}, and CycleGAN~\cite{Zhu_2017_ICCV}. Therefore, we compare the proposed model against StarGAN.

We also adopt ResidualGAN~\cite{shen2017learning} as a baseline, which performs attribute transfer with the residual image learning. Both StarGAN and ResidualGAN belong to ~\emph{Grouped} methods, in which they acquire group-labelled training data. More importantly, ResidualGAN acquires an equal number of images between the MI group and the MA group for the highly unbalanced attribute during its training process. Unlike ResidualGAN, StarGAN only uses group-labelled data, in which it does not adopt specific measures for coping with unbalanced training data. It is worth noting that for multi-attribute manipulation tasks, we must train ResidualGAN many times. In this comparative study, we implement ResidualGAN on our own and use the official code of StarGAN \footnote{Please see https://github.com/yunjey/StarGAN}.

\subsection{Dataset}

The CelebFaces Attribute Dataset (CelebA)~\cite{liu2015deep} contains 202,599 face images with large pose variations and background clutter. We cropped and scaled each image to 128$\times$128 pixels. 5000 images are randomly selected as the test dataset with the remaining images used as the training dataset. In our experiments, we would select and divide the facial attributes into balanced parts, e.g., gender, hair color (black and blond hair), smile and unbalanced parts, e.g., pale skin. For balanced attributes, we train a model for multiple attributes manipulation. Note that, we train a single model for every unbalanced attribute.

\subsection{Baseline Comparison}
In this section, we provide comparison results with the baseline methods in balanced attributes transfer tasks and use some metrics to evaluate the translation results from multiple perspectives.

{\bfseries Qualitative evaluation:}
Fig.~\ref{fig:comparison} shows facial attribute manipulation results generated by SG-GAN for three sparsely grouped datasets, which respectively have 500 samples, 5000 samples for every value in the attribute, and the full dataset group-labelled. To distinguish these models, we denote them as SG-GAN(500), SG-GAN(5000), SG-GAN(All) respectively. As shown in the 3rd to the 5th rows of Fig.~\ref{fig:comparison}, the quality of translation results does not noticeably decline when the number of group-labelled attributes is reduced.

In comparison with ResidualGAN, our method attains a higher visual quality of translation results. One important reason is that ResidualGAN uses the vanilla GAN loss which may not provide the stable gradient in its training process. Another is that training a model to perform a fixed translation is prone to overfit~\cite{choi2017stargan}. The proposed model using the WGAN-GP loss term does not suffer from the same problem when applied to translate multiple image attributes.

Furthermore, compared to StarGAN, the proposed model demonstrates its superior advantage in keeping the unrelated content fixed, for example, image background. It can be interpreted that the adapted residual image learning component as adopted by the proposed model can effectively help the information translation process and chooses a good initial point for its training process.

{\bfseries Quantitative Evaluation Protocol:}
Through qualitative evaluation, the advantages of the proposed method in attaining a high image translation quality and preserving other visual attributes unrelated for the translation task are further demonstrated.

To assess quantitatively the sample quality and degree of translation, we compute the classification accuracy using the ResNet-18~\cite{he2016deep}, which is the same evaluation network used in StarGAN~\cite{choi2017stargan}. We notice that the classification accuracy of translation results concerning the targeted attributes does not consider the preservation of unrelated image attributes in a translation process. Therefore, we not only compute accuracy on targeted attributes but also other unrelated facial attributes to explore whether there is any side effect of accidentally modifying visual attributes unrelated in a translation process. Here we select three attributes: gender, smile and hair color in this experiment.

Additionally, to show the ability keeping the consistency of irrelevant content, for example, image background, we use MS-SSIM~\cite{wang2004image} to measure the similarity in image background between the translation results and input samples. A higher MS-SSIM value corresponds to a higher similarity between images in human perception. For specifically, we crop the 10 $\times$ 10 top-left corner from both translation results and input samples as the background region.

{\bfseries Quantitative evaluation:}
As shown in the first column of Table~\ref{tab:gender_classification}, we give the classification accuracy of attribute gender on translation results of attribute gender, which indicates SG-GAN achieves an acceptable degree of translation.

The accuracies of other attributes, smile and hair color, on this translation results are shown in the second and third columns. In the case of attribute smile, SG-GAN(All) achieves the accuracy of 86.87, higher than both baseline models. For hair color, our model and baseline models have the similar accuracy. It indicates that our model and StarGAN have some advantages in generated images with respect to the maintenance of identity in general. Additionally, our model is more capable to keep background fixed, e.g, 72 for SG-GAN(All), 70 for SG-GAN(500), 64 for StarGAN and 63 for ResidualGAN, showing in the last column. More importantly is that SG-GAN(500) and SG-GAN(5000) obtains closed scores compared with SG-GAN(ALL) in most of cases. Table~\ref{tab:hair_classification} reports scores on translating hair color as the targeted attribute, which produce the similar conclusion.
\begin{table}
\vspace{-0.4cm}
\scriptsize
\caption{Classification accuracy [\%] of attribute gender transfer results for different models.}
\vspace{-0.4cm}
\label{tab:gender_classification}
\begin{center}
\begin{tabular}{cccccc}
\toprule
Attribute & Gender & Smile & Hair Color & Background \\
\midrule
ResidualGAN & 95.49 & 85.80 & 95.90 & 63 & \\
StarGAN & 96.23  &  85.99 & {\bfseries 99.63} & 64 &\\
SG-GAN(ALL) & {\bfseries 99.03} &  86.87 & 97.99 & {\bfseries 72} &\\
SG-GAN(5000) & 93.79 & {\bfseries 87.34} & 98.42 & 71 &\\
SG-GAN(500) & 93.36 & 84.87 & 97.90 & 70 &\\
\bottomrule
CelebA & 99.00 & 90.22 & 99.53 & 100 & \\
\end{tabular}
\end{center}
\vspace{-0.5cm}
\end{table}

\begin{table}
\scriptsize
\vspace{-0.3cm}
\caption{Classification accuracy [\%] of attribute hair color translation results for different models.}
\vspace{-0.4cm}
\label{tab:hair_classification}
\begin{center}
\begin{tabular}{cccccc}
\toprule
Attribute & Gender & Smile & Hair Color & Background \\
\midrule
ResidualGAN & 83.15 & 86.07 & 93.41 & 18 & \\
StarGAN & {\bfseries 99.43} & 88.26 & {\bfseries 99.53} & 63 &\\
SG-GAN(ALL) & 91.38 &  87.42 & 98.99 & {\bfseries 73} \\
SG-GAN(5000) & 89.58 & 87.36 & 95.72 & 72 &\\
SG-GAN(500) & 89.11 & {\bfseries 88.68} & 88.91 & 69 &\\
\bottomrule
CelebA & 99.00 & 90.22 & 99.53 & 100 & \\
\end{tabular}
\end{center}
\vspace{-0.5cm}
\end{table}

\subsection{Translation experiments on unbalanced attributes}

\begin{figure}[t]
\vspace{-0.2cm}
\begin{center}
\includegraphics[width=1.0\linewidth]{figure_unba_intro.pdf}
\end{center}
\vspace{-0.4cm}
\caption{Probability of correct classification for the samples shown in Figure~\ref{fig:comparison_unb} in different models. As shown in the left, it is obvious that StarGAN would mistake the sample from MI group as MA group when the unbalance between MI and MA group is very serious.}
\vspace{-0.4cm}
\label{fig:figure_unba_intro}
\end{figure}

\begin{figure}[t]
\begin{center}
\includegraphics[width=1.0\linewidth]{figure_archi_dis.pdf}
\end{center}
\vspace{-0.5cm}
\caption{More comparison between StarGAN and SG-GAN(ALL): (a) both are trained on three attributes, balanced: gender, hair color; unbalanced: pale skin. (b) The facial attribute manipulation results of StarGAN, when it is trained on a sparsely grouped dataset without using the reconstruction loss.}
\label{fig:figure_archi_dis}
\vspace{-0.3cm}
\end{figure}

\begin{figure}[t]
\vspace{-0.3cm}
\begin{center}
\includegraphics[width=0.9\linewidth]{figure_ablation.pdf}
\vspace{-0.4cm}
\end{center}
\caption{The validation of effectiveness for adapted residual image learning. First row is the multiple attributes manipulation results without residual learning; 2rd is the results with the original residual image learning method; 3rd is the results with the proposed adapted residual image learning.}
\label{fig:ablation}
\vspace{-0.5cm}
\end{figure}

In this subsection, we will show the translation results on unbalanced attributes\,(i.e. pale skin). As shown in Fig.~\ref{fig:comparison_unb}, our method and baseline methods give out translation results for attribute pale skin on the training dataset with the different number of the MI group. Concerning the pale skin attribute on the CelebA dataset, the numbers of positive and negative labels in the training dataset are 8494 and 189106 respectively.

For ResidualGAN, requiring the same number of MI group and MA group, its translation results lack quality and visibility, worse and worse with the decrease in the number of MI group. Obviously, ResidualGAN could not generate realistic samples when just using 10 images for single group~($1$-th row of Fig.~\ref{fig:comparison_unb}).

StarGAN does not require the balance between the number of images between the MA group and MI group. As shown in the $2$-th row of Fig.~\ref{fig:comparison_unb}, StarGAN achieves excessive translation results from MA group to MI group of pale skin, but not obvious translation conversely. As shown in Fig.~\ref{fig:figure_unba_intro}, the reason of this phenomenon is that StarGAN does not have fine classifier after training on the unbalanced dataset and mistakes the samples of MI group for MA group. StarGAN is based on conditional GAN~\cite{DBLP:journals/corr/MirzaO14} and it requires the group-label of the target domain $c$ when images translate from the source domain to the target domain. However, the translation results from the the MI group to the MA group could not be obvious when $G$ of StarGAN thinks some samples from the MI group have the same domain with the MA group. The more obvious translation results have been shown in Fig.~\ref{fig:comparison_unb}~(See 5th column) when StarGAN has been trained in the balanced dataset, which contains 8494 images from MA group and 8494 images from MI group.

Similar to ResidualGAN, SG-GAN requires the balance between MI group and MA group. But the difference is that SG-GAN, which works well on sparsely grouped datasets, can make full use of unlabelled data to stabilize the training of networks. When the MI group is very small, SG-GAN still generates very high-quality samples. Compared with StarGAN, SG-GAN can be trained to obtain
a fine classifier. As shown in the 3rd row of Fig.~\ref{fig:comparison_unb}, SG-GAN could achieve high-quality results and very obvious translation effect, whether it is translating the attribute of pale skin from the MI group to MA group or in the reverse direction. We notice that image translation results of SG-GAN are also satisfactory when the number of MI group are very small, e.g. having only 10 data points in our experiments.

Because of requiring the multi-attribute group-labels as the target domains, StarGAN would suffer the serious problems that the manipulation results for the single attribute is easy to be affected by other attributes, especially when trained on the unbalanced attribute. As shown in Fig.~\ref{fig:figure_archi_dis}(a), the attribute pale skin affects the manipulation results of other attributes. When trained on the same dataset, however, SG-GAN does not suffer from this problem. As mentioned above, it is hard for StarGAN to be trained on the sparsely grouped dataset, because of the lack of original domain labels to assess the reconstruction loss. As shown in Fig.~\ref{fig:figure_archi_dis}(b), StarGAN without the reconstruction loss could not obtain the high-visual quality translation results when trained on the sparsely grouped dataset. These comparison results show the superiority of our architecture.

\begin{table}
\vspace{-0.4cm}
\scriptsize
\begin{center}
\caption{Classification accuracy of translated results on SG-GAN(500) using residual image learning or adapted residual image learning.}
\vspace{-0.2cm}
\label{tab:ablation study}
\begin{tabular}{cccccc}
\toprule
Method & Gender & Smile & Hair Color \\
\midrule
Residual Image Learning & 18.77 & 23.67 & 21.35 & \\
Adapted Residual Image Learning  & {\bfseries 93.26} & {\bfseries 82.67} & {\bfseries 88.91} &\\
\bottomrule
\end{tabular}
\end{center}
\vspace{-0.6cm}
\end{table}

\subsection{Ablation Study}
In this section, we validate the effectiveness of the adapted residual image learning component in the proposed method by comparing translation results generated by SG-GAN with several of its variants. As shown in Fig.~\ref{fig:ablation}, without residual image learning, SG-GAN can only generate low-quality images that appear blurry and fail to preserve visual characteristics uninvolved in the translation task. In comparison with the original residual image learning method~\cite{shen2017learning}, the adapted residual image learning component improves the degree of images translation. For the original residual learning, the summation between the real images and the network output would be the final translation result. However, the learned residual image tends to become sparse and the output becomes close to the input when the adversarial loss has sufficient capacity. The proposed method solves this by concatenating the real images and the feature maps of network $D$ instead of summing them up linearly. As shown in the Table~\ref{tab:ablation study}, the quantitative evaluation validates the effectiveness of this design.

\section{Conclusion}
We have proposed a new model, i.e. SG-GAN, to perform multiple facial attributes manipulation with one-input multi-output architecture, which is well suited for tackling multi-task learning and sparsely grouped learning tasks. Tightly coupled with this learning architecture, an adaptive residual image learning paradigm has been shown to enhance the performance of the new method in image translation. As consistently demonstrated results reported in the paper, the proposed method is able to attain comparable image translation results as multiple state-of-the-art peer methods while having access to significantly fewer training group-labels. Moreover, the experiments show that SG-GAN is able to consistently achieve more apparent translation results over the dataset for unbalanced attributes. It is interesting and valuable to apply SG-GAN for more IIT tasks, for example, age progression and regression of facial image.

{\small
\bibliographystyle{ieee}

redpajama_set_name: RedPajamaArXiv


text:
Tarará is a gated resort town in Havana, Cuba.

Tarara may refer to:

Nichifor Tarara, Romanian sprint canoer and 1958 Olympic bronze medalist
Stefan Tarara, third place violinist in the 2010 Paganini Competition and 2011 Henryk Wieniawski Violin Competition
Tarara, a character in Utopia, Limited, a Gilbert and Sullivan opera
Tarara, a name given to the members of the Croatian diaspora in New Zealand by Māori
Tarara, New Zealand, a locality in South Otago

redpajama_set_name: RedPajamaWikipedia


Coke vs Pepsi. Tom vs Jerry. Microsoft vs Apple. Right up there with these world famous rivalries has to a rivalry as old as time – The eternal tussle between coffee and tea! Each drink has its loyal supporters, with coffee drinkers being associated with that busy buzz of energy while tea drinkers are seen to be zen and restive. Obviously, there are benefits to both beverages but is one really better than another? Let's find out!
It's no secret that coffee is the choice of drink if you want that sudden burst of energy. It only takes 10 minutes for your body to feel the effects of caffeine. One cup of coffee can easily pack about 80mg per cup ( almost 1/5 the recommended daily caffeine intake for men and 1/3 for women), so that's a quick morning pick me up. But, this could also be psychological. In a 2011 study, researchers discovered that daily coffee drinkers who were tricked into drinking decaf still scored the same on tests as caffeinated coffee drinkers. We're so used to associating coffee with energy that we intuitively expect it to provide a jolt, even if it isn't there!
While tea can pack a fair amount of caffeine, it also contains an amino acid called L-theanine.This tiny chemical creates two reactions in your body; the first, it lowers the absorption speed of caffeine and the second, it stimulates the neurotransmitter GABA in your brain, which has anti-anxiety effects. This creates an alert but relaxed state, which explains why tea is popularly used to aid meditation.
While caffeine has been shown to slightly reduce appetite, we've all heard the good news about green tea. More than the caffeine, the EGCG in green tea can help shrink fat cells and makes muscle cells more active.
While tea might be better for burning fat, coffee is the drink of choice when you want more muscle. An article in the Journal of Medicine and Science in Sports and Exercise discovered that men who drank 2.5 cups of coffee could sprint up to 9 percent more than when they didn't have coffee. It is likely because caffeine stimulates your muscles, which can help you power through with more reps. However, the article was careful to state that the coffee had to be drunk a few hours before their workout.
While tea also packs some caffeine and can cause sleeplessness, too much coffee in your system makes it harder for you to sleep as each cup packs a significant amount in comparison to tea. Lesser known is that too much coffee can make it hard for your body to absorb magnesium. As it is, many of us don't get enough magnesium. Having too much coffee, coupled with this common nutrient deficiency, can cause symptoms like muscle cramps and sleeplessness.
The long list of health benefits of tea, especially green tea and white tea, is common knowledge. Long story short, green tea is loaded with antioxidants such as flavonoids, EGCG and catechins that make green tea such a great catch! These chemicals recharge the white blood cells and help prevent viruses from reactivating. They also help fight inflammation and prevent blood vessels from hardening. This is also why tea is associated with anti aging.
Tea is also known to boost brain health; tea drinkers who drank more than 2 cups of green tea a day had a significantly lower risk of age-related declines in memory, compared to those who had less than 3 cups a week. Still, coffee does have its benefits. Similar to tea, it also contains antioxidants, though significantly less than tea. Like tea, which improves insulin sensitivity, coffee is believed to reduce the risk of type 2 diabetes. Each cup of coffee contributes to almost a 7% reduction in developing this disease. It also plays a part in protecting the liver and reduces the risk of cirrhosis. Coffee also contains key nutrients such as potassium, manganese, magnesium, and niacin.
However, one area where coffee loses out to tea is bone health. Regular tea drinkers are likely to have higher bone density levels and slower rates of bone erosion. While it remains unclear how significant the effects of coffee on bone density is, one study found that 4 or more cups a day reduced bone density up to 4 percent. Coffee is also notorious for causing acid reflux. Coffee can relax the band of muscle between your oesophagus and stomach. When that space opens, stomach acid could splash back up and cause acid reflux.
The preparation of coffee can also affect its health value. Unfiltered coffee, like espresso or French press, may increase "bad" LDL cholesterol, which could increase your risk of heart attack and stroke. This increased risk is because of oily substances called diterpenes. Cafestol and Kahweol are the two significant diterpenes in coffee. They are present either as oily droplets or in the grounds floating in the coffee.
It is common knowledge that coffee can yellow teeth badly, so for a long time, tea was touted as a credible alternative. Green tea is also believed to promote oral health as the antioxidants in green tea can actually kill bacteria in your mouth and keep you from getting an infection. However, the tannins present in tea are acidic and will break down the enamel on teeth. Once this enamel layer is eroded, pigmented molecules are easily able to attach to teeth and this leads to staining. It is unclear whether this means that green tea, which is rich in tannins, can lead to more staining!
Use ground coffee to make a scrub and exfoliate your scalp. If you have dark hair, good news! Coffee can also impart an incredible shine to hair. Follow it up with a tea hair rinse to boost hair health. Tea is also a natural alternative to dyeing your hair. While terrible for your teeth, its staining properties add colour to naturally blonde or brunette locks. Coffee is a known diuretic. While this is bad news when you drink up, it's great news when you apply it topically! The same diuretic effect draws fluid away from fat cells, making them shrink. This tightens your skin, temporarily minimising the visibility of cellulite.
Tea is very useful as a soother for sunburns and bug bites. Cold compresses made from black tea bags can relieve pain and reduce redness. And if you are having trouble with foot odour, cooled boiled tea is your friend! The tannic acid in tea is both antibacterial and antifungal, so it stops feet from sweating and smelling funky.
While we might seem a bit biased, the truth is tea is probably much better for your overall health. While coffee has its benefits, you do need to be a regular drinker to reap them. But the caffeine content does add up, which is likely to cause over stimulation and affect sleep quality. However, you can get your coffee fix by using it externally as it is a skin and hair boost.


we took him camping. we hauled diapers, footed jammies, 568 onesies. a pack and play, a bouncer. i nursed him on beaches, in tents, while sitting in those really uncomfy folding chairs that are for some reason quite popular. jeremy walked and bounced him for hours in 95 degree weather. i entertained him, in the dark, quietly, during the fine hours of 3-5 a.m., while the rest of the family slept soundly beside me.
he rewarded these dedicated efforts with the first real, true grins of babyhood. oh my cuteness.
a fine trade off, if i do say so myself.


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Special-needs youth gain bipartisan inspiration, guidance
Patrick B. McGuigan
By Patrick B. McGuigan
Published: 05-Jul-2010
In a recent slice of "real world" discussion and learning that mirrored events in this year's legislative session, students working through the Oklahoma Developmental Disabilities Council (OKDDC) learned about the law-making process during a week-long session held last month.
Concluding activities as part of the Youth Leadership Forum, the young people heard from a bipartisan group of government officials, studied public policy and interacted with each other. Time at the Capitol in Oklahoma City echoed what transpired when legislators grappled with special needs scholarships this year.
After hearing from a bipartisan group of legislators over the closing two days of the forum, the young people met in the chambers of the state House of Representatives, to debate House Bill 3393, deemed the "Lindsey Nicole Henry Scholarships for Students with Disabilities Program."
Sitting in the seats of elected officials, requesting recognition from a presiding officer before speaking, and addressing one another with respect, the students challenged premises and frequently sparred over provisions allowing tax-financed scholarships to special needs children.
After advocacy and opposition — floor statements, debate, questions and answers on both sides — the young legislators approved the bill, 14-10.
The close division among members of the Youth Leadership Forum mirrored the narrow division the legislation provoked in the real Legislature, where it passed the House 54-46 and the Senate 25-22. Governor Brad Henry signed it.
Although debate on H.B. 3393 was of interest to CapitolBeatOK, it was just one segment of two days full of civics lessons and interaction with well-know state leaders.
In response to question from CapitolBeatOK, Director Ann Trudgeon of the council describes it as "a small state agency. We are authorized by Executive Order of the Governor, administratively supported by DHS [the Department of Human Services], and almost entirely federally-funded.
"Developmental Disabilities Councils are in every state and most U.S. Territories, and we are established for the purpose of advocating for improved services and supports for persons with developmental disabilities. I think of Councils as 'research and development' for public and private entities wishing to meet the needs of persons with developmental disabilities and their families."
The Oklahoma council was established by an executive order of the state's governor in 1973, and renewed by every chief executive since then. The council is not a service provider, but "has assisted thousands of Oklahomans to live, work, play, learn and worship in their own homes and in their own neighborhoods." Former state Rep. Mark Liotta is chairman of the council.
Trudgeon said councils around the nation watch each other for "innovative program and project ideas." California originated the "Youth Leadership Forum," which developed "as a means to address some basic needs of students with disabilities – namely, limited focus on transition from high school to adulthood, and all that that entails."
Trudgeon said, "Advocates for people with disabilities believe that too often youth with disabilities are only focused on the next year – not the big picture of life. The Youth Leadership Forum seeks to compel students with disabilities to take action and responsibility for their futures."
In a week-long program, she said, "we work with students on college and career exploration, becoming involved in the development of their IEPs (Individualized Education Plans), understanding and embracing their disability and learning about 'disability' as a culture, learning about resources in their communities (generic and specialized) so they can lead independent lives, and becoming more involved in their communities — through volunteerism and civic involvement."
Youth Leadership Forums have been held in Oklahoma since 2004. Focus of the program is a week on a state college campus. According to OKDDC materials, the week covers "leadership, assistive technology, independent living, higher education, career development options and the Oklahoma legislative process."
CapitolBeatOK witnessed a portion of the Youth Leadership Forum's annual Capitol Day. One guest speaker was state Rep. Ben Sherrer, a Democrat from Pryor Creek. Focused on legislative process, Trudgeon said, "He presented the topic beautifully and was impressed by the questions our youth asked."
After learning about law making, students were introduced to "the highly topical and fairly controversial House Bill 3393. After the bill was explained, the youth broke into two groups – pro and con – and worked on their presentations at the Capitol for the following day."
Students in the "pro" group selected a bill author, who was "prepped" to present the bill to fellow "legislators" and be prepared to answer questions.
Trudgeon outlined the counselors' part this way: "We gave both groups sample questions, but also encouraged development of their own questions about the bill. Additionally, we gave each group talking points, which they turned into debate statements. Both groups developed debate points other than those we presented."
In the morning, state Rep. Jason Nelson, an Oklahoma City answered questions about his bill, H.B. 3393. Trudgeon observed, "We've always used real bills for these sessions, but this was the first time we had a legislative author make a presentation to the students." State Rep. Anastasia Pittman, an Oklahoma City Democrat and a co-author of Nelson's bill, later witnessed closing debate on the measure.
Trudgeon said a highlight of the day was a meeting the young people had at the Blue Room with Lt. Gov. Jari Askins, a Democrat. Trudgeon told CapitolBeatOK she experienced a preface to the floor debate that came later.
Before Askins entered the room, Trudgeon sat with the student "author" of H.B. 3393. The lad "grabbed my hand, and with tears in his eyes told me he was incredibly nervous about standing before his peers and presenting his bill. He asked me what would happen if he made a mistake while making his presentation. I asked him what happened when he made mistakes in other aspects of his life, and he replied, 'People make fun of me.'"
Trudgeon reports the young man is an "out-going, seemingly very self-assured young man. He was always the first to volunteer to help, he made it a point to speak with and get to know all his peers at YLF [Youth Leadership Forum], and he was generally interested in all that was going on during YLF."
Surprised by his worries, Trudgeon reports she "assured him that all people make mistakes, and all each of us can do is move forward. I gave him a couple of suggestions" on what to do if he made a mistake during presentation.
Then, the lieutenant governor entered the room. She "addressed the very same issue in her talk with the youth." Naturally, she said, "That helped quite a bit."
Trudgeon suggested the young man ask that Rep. Nelson sit nearby during discussion of the bill, "in case he had any questions … he was not able to answer. Rep. Nelson, of course, was very pleased to do so." Although the boy "was visibly nervous, he introduced the bill and was able to very successfully answer questions."
Each student explored the Capitol, meeting with legislators when possible but in all cases hand delivering personal notes to the elected officials. Several legislators had returned to the Capitol to be available. Trudgeon said many of the lawmakers had certificates prepared ahead of time, waiting in their offices when the young people dropped off their notes. Members of Askins staff, Nelson and state Rep. Lewis Moore, a Republican from Arcadia, joined the students for lunch.
Public speaking is a key goal at Capitol Day, and Trudgeon said, "we were pleased with the efforts of our very nervous youth – the House Floor can be pretty intimidating!"
Concerning debate and discussion on H.B. 3393, Trudgeon said, "We encouraged each to vote his/her conscience based on what they'd read and learned – and not with reference to how they debated. Several 'crossed over' and voted opposite of how they debated, which made me very proud."
Concluding Capitol Day, students stopped at Progressive Independence, an Independent Living Center in Norman, on their way "home" to Chickasha. After dinner at the University of Sciences and Arts of Oklahoma, home of this year's forum, the students discussed and processed their day until 9 p.m.
Trudgeon told CapitolBeatOK the serious day concluded with "the mother of all water gun fights – these are, after all, kids. And that, perhaps, is our biggest lesson. It's not about disability – it's about being who you are and making yourself and your life the best you can."
Pat McGuigan
CapitolBeatOK
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Tickets are going fast for the biggest trivia night in St. Louis benefiting Carol House Quick Fix Pet Clinic as well as 12 other local pet welfare organizations. New Year For The Strays is taking place on January 9, 2016. Tables of 10 are $400 or $40 per ticket. Tickets include complimentary Barefoot wine, Anheuser-Busch beer, soda and water PLUS special gifts. You can also "upgrade" your basic table for an additional $75, which includes 2 Imo's pizzas, 10 – 50/50 tickets and a dedicated server. This event sells out every year. Don't wait! All the money stays right here in St. Louis to help pet adoption, spay/neuter and educational programs.


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Shopify Inc. to acquire a robotics warehouse fulfillment vendor
Staff and Bloomberg report | Sep 10, 2019
Shopify will acquire 6 River Systems Inc. for $ 450 million to add its software and robots to its Shopify Fulfillment Network.
Ecommerce platform provider Shopify Inc. will acquire 6 River Systems Inc., a robotics warehouse fulfillment vendor, for $450 million, Shopify announced this week.
Ecommerce platforms evolve beyond websites and apps
Katie Evans | Sep 4, 2019
The vendor's cloud-based software and mobile robots named "Chuck" will be added to Shopify's Fulfillment Network and will help Shopify's warehouse associates with daily tasks, including inventory replenishment, picking, sorting and packing. Shopify expects this to increase the speed and reliability of its warehouse operations and is a "critical step to accelerate its growth," Shopify says.
In June, Shopify announced plans to spend $1 billion over the next five years to set up a network of fulfillment centers in the U.S. to help merchants using its ecommerce platform deliver products to shoppers more quickly and cheaply and to better compete with Amazon. A Shopify spokeswoman says the company has negotiated low rates with warehouses and shipping companies for its merchant customers.
The response has exceeded the vendor's expectations, Shopify chief operating officer Amy Shapero said on Shopify's second quarter earnings call on Aug. 1, noting that "thousands" of merchants have expressed their desire to be a part of Shopify's early-access program.
The vendor works with more than 820,000 merchants in 175 countries, and the Shopify Fulfillment Network aims to provide the "best shipping and fulfillment rates" for its merchants, says a company spokeswoman. Within Internet Retailer's Top 1000, 37 online retailers use Shopify as their ecommerce platform. It is ranked as the No. 5 vendor of ecommerce platforms in Internet Retailer's forthcoming Leading Vendors report.
What's more, preliminary results from Internet Retailer's Ecommerce Technology Survey finds that Shopify is one the most-used ecommerce platforms, according to survey results from 130 retailers. Plus, of the 28.5% of retailers that said they were looking to switch ecommerce platforms, 23% plan to switch to Shopify, according to early survey results.
With the 6 River acquisition, Shopify gains important talent from the vendor, says Samad Samana, senior vice president at investment banking firm Jefferies Group LLC, as two of its executives have previously worked at Amazon.com Inc.
"The deal gives Shopify the 'technical know-how' as it adds intellectual property to its broader fulfillment ambitions," says Brian Peterson, a vice president at investment banking firm Raymond James Financial.
The robots are coming—and they're bringing your online order
James Melton | Sep 3, 2019
6 River was founded by executives who came from Kiva Systems—now Amazon Robotics—and it operates in more than 20 facilities across the U.S., Canada and Europe. It fulfills orders for companies including Lockheed Martin Corp. and Office Depot Inc.
The deal, poised to close in the fourth quarter, is expected to increase Shopify's expenses by about C$25 million ($19 million) in 2019, with no material impact on its revenue for the year.
Of the $460 million sales price, 60% of that is in cash and the rest in voting shares, according to a statement from both companies. Shopify fell as much as 4.9% in New York to the lowest in a month, extending the stock's slump for a third day.
Katie Evans contributed to this article.
Shopify adds fulfillment service for small merchants
Bloomberg News | Jul 23, 2019
Shopify plans to set up a fulfillment network in the US
Bloomberg News | Jun 19, 2019
Online storefronts represent 80% of Shopify merchants' sales
Katie Evans | Jun 10, 2019
Shopify bans retailers promoting hate groups and violence
Bloomberg News | Nov 6, 2018


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1999 NHL Regular Season Individual Leaders
Updated February 11, 2017 | Infoplease Staff
(*) indicates rookie eligible for Calder Trophy.
2000 NHL All-Star Game
Team Goaltending, 1999-2000
.com/ipsa/0/8/5/5/6/2/A0855625.html


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Publications and Manuals
Operational instructions and guidelines
Operational Bulletins 2011
ARCHIVED – Operational Bulletin 318 - June 27, 2011
We have archived this page and will not be updating it.
You can use it for research or reference.
This section contains policy, procedures and guidance used by IRCC staff. It is posted on the department's website as a courtesy to stakeholders.
Updated Ministerial Instructions: New Cap on Federal Skilled Worker Applications
This Operational Bulletin has expired.
Effective July 1, 2011, a cap of 10,000 new Foreign Skilled Worker (FSW) applications, without an offer of arranged employment, will be considered for processing each year. Within the 10,000 cap, a maximum of 500 new applications per occupation will be considered each year. Instructions are given to the field on what to do with applications received before and after this date.
This Operational Bulletin (OB) provides guidance on FSW applications and the third set of Ministerial Instructions (MI-3), which come into force on July 1, 2011.
On June 18, 2008, the Immigration and Refugee Protection Act was amended to give the Minister of Citizenship and Immigration the authority to issue instructions that would ensure the processing of applications and requests be conducted in a manner that, in the opinion of the Minister, will best support the attainment of immigration goals set by the Government of Canada.
The first set of Ministerial Instructions (MI-1) was issued on November 29, 2008 and established that new FSW applicants would be eligible for processing only if they had experience in one or more of the identified eligible occupations; if they had arranged employment; or if they had one year of legal, in-Canada work experience and status as a temporary foreign worker or international student. Under MI-1, all FSW applications were also submitted to the Centralized Intake Office (CIO) in Sydney, Nova Scotia, for an initial eligibility review. The final determination for eligibility and selection remained with the visa office.
The second set of MI (MI-2), issued on June 26, 2010, introduced a number of changes, including a revised list of eligible occupations, annual limits on the number of new applications to be processed in the FSW class, as well as revised eligibility criteria with respect to evidence of official language proficiency and work experience. In addition, the CIO was given the authority to make final eligibility determinations.
The third set of MI (MI-3) comes into force on July 1, 2011, and applies only to applications received by the CIO on or after this date. MI-3 includes changes to the following programs:
Federal Entrepreneur Program: A temporary moratorium on new applications will be placed on this program (see OB 319 for more information).
Federal Immigrant Investor Program (IIP): A cap of 700 new IIP applications will be considered for processing each year. In addition, as of July 1, 2011, all new federal Immigration Investor applications must be submitted to the CIO (see OB 320 for more information).
Federal Skilled Worker Program (FSW): A cap of 10,000 new FSW applications, without an offer of arranged employment, will be considered for processing each year. Within the 10,000 cap, a maximum of 500 new applications per occupation will be considered each year.
The full text of these instructions can be found at http://www.gazette.gc.ca/rp-pr/p1/2011/2011-06-25/html/notice-avis-eng.html#d119
Overview of Ministerial Instructions for FSW Applications – July 1, 2011
MI-3 applies only to applications received by the CIO on or after July 1, 2011. All complete FSW applications received by the CIO prior to July 1, 2011 will continue to be considered for processing with regard to the requirements in effect at that time.
Complete FSW applications [note 1] received by the CIO on or after July 1, 2011, not exceeding the identified caps and meeting either of the following criteria shall be placed into processing:
Applications submitted with an Arranged Employment Offer consistent with requirements of subsection R82(2) of the Immigration and Refugee Protection Regulations;
Applications from skilled workers with evidence of experience in the last ten years under one or more [note 2] of the National Occupation Classification (NOC) codes specified in the MIs and do not exceed the identified cap of 10,000 new complete applications per year, with no more than 500 new applications in any one NOC code.
Limit on the number of applications to be processed per year
A maximum of 10,000 new FSW applications without an offer of arranged employment will be considered for processing each year.
Within the 10,000 cap, a maximum of 500 new FSW applications per NOC code will be considered for processing each year. The list of eligible NOC codes under MI-3 remains the same as those found in MI-2.
Complete applications supported by a valid offer of arranged employment, consistent with the requirements of R82(2), will be considered for processing without regard to either the global cap or the occupational list and its associated caps.
In calculating the caps, applications will be considered according to the date they are received. Applications received on the same date will be considered for processing with regard to routine office procedures.
For the unique purpose of calculating the caps, the cap year will begin on July 1, 2011, and end on June 30, 2012, unless otherwise directed in subsequent MIs.
Guidelines for new and existing applications
All new FSW applications will be considered according to the date they are received.
FSW applications received at the CIO before July 1, 2011
The third set of MIs does not apply to complete applications received prior to July 1, 2011. All FSW applications received by the CIO prior to this date shall continue to be considered for processing having regard to the requirements in effect at that time.
FSW applications received at the CIO on or after July 1, 2011
MI-3 applies to applications received at the CIO on or after July 1, 2011. The CIO will assess complete applications received on or after July 1, 2011 against the MIs to determine whether applicants are eligible for processing. The CIO will make a final eligibility determination, and those determined to be eligible will be placed into processing. Applications that receive a negative eligibility determination will not be processed and will receive a full refund of the processing fee. The application and documentation submitted will not be returned.
Submitting the application
All new FSW applications will continue to be sent to the CIO. Applicants are required to submit their complete application, together with all supporting documents, to the CIO. This includes all documents listed both on the CIO and document checklists specific to the visa office.
The CIO will review applications for completeness and, if the application is incomplete, the entire application package will be returned to the applicant.
Final eligibility determination at the CIO
The CIO will assess the applicant's submission based on the documentation submitted and make a final determination of eligibility. To be eligible for processing, the applicant must meet all the criteria described in the MIs. If the applicant's submission is determined to be ineligible for processing, the applicant will be informed and will receive a refund. The application and documentation submitted will not be returned. If the CIO makes a positive eligibility determination, the application will be placed into processing and the applicant will no longer be eligible for a refund.
Updates to the OP 6 manual chapter are forthcoming.
For further information on changes outlined in this OB, please contact Operational Management and Coordination Branch.
1. In order to be considered, applications must be completed according to the application kit requirements in place at the time the application is received by the Centralized Intake Office.
2. Applicants will have one year of continuous full-time or equivalent paid work experience in at least one of the listed NOCs and not combined partial year experience in multiple NOCs. This work experience will reflect the actions described in the lead statement for the occupation as set out in the occupational descriptions of the NOC, including the performance of a substantial number of the main duties and all of the essential duties described.


Fenix CL09 is a compact, elegant and cold-resistant camping lantern. Neutral white light effectively gives better color rendering, while the featured multipurpose red and green lights can be used as signal lights.
Tighten the lantern tail to turn on the lantern. Loosen to turn off the lantern.
To change output level while the lantern is switched on, turn it off and back on again within one second. Lantern will cycle through Low→Med→High→Turbo→Red→Red Flash→Green.
Note: the lantern is not equipped with a memory function. If more than one second elapses after being switched off, it will default to Low mode when switched on again.
Unscrew the lamp cap , take out the battery, plunge the charging cable into the battery built-in micro USB port. Or charge the battery in a charger.
When charging via micro USB port, the charging indicator turns red, and blue when fully charged.
When fully charged, disconnect the charging cable or remove the battery from the charger, and insert the battery with the anode side (+) towards the lamp head, then screw the tail cap back on.
Recharge a stored battery every four months to maintain optimum performance of the battery.


19	// On Unix, this map is augmented by readProtocols via lookupProtocol.
30	// (some Solaris distros, nacl, etc).
31	// On Unix, this map is augmented by readServices via goLookupPort.
88	// functions and by Dialers without a specified Resolver.
91	// A Resolver looks up names and numbers.
93	// A nil *Resolver is equivalent to a zero Resolver.
97		// GODEBUG=netdns=go, but scoped to just this resolver.
101		// TODO(bradfitz): Timeout time.Duration?
104	// LookupHost looks up the given host using the local resolver.
105	// It returns a slice of that host's addresses.
110	// LookupHost looks up the given host using the local resolver.
111	// It returns a slice of that host's addresses.
113		// Make sure that no matter what we do later, host=="" is rejected.
114		// ParseIP, for example, does accept empty strings.
124	// LookupIP looks up host using the local resolver.
125	// It returns a slice of that host's IPv4 and IPv6 addresses.
138	// LookupIPAddr looks up host using the local resolver.
139	// It returns a slice of that host's IPv4 and IPv6 addresses.
141		// Make sure that no matter what we do later, host=="" is rejected.
142		// ParseIP, for example, does accept empty strings.
155		// uses a context key instead of unexported variables.
170			// complete. See issue 8602.
189	// The return values are (IPAddr, error).
193	// the return values from LookupIP.
207	// ipAddrsEface returns an empty interface slice of addrs.
216	// LookupPort looks up the port for the given network and service.
221	// LookupPort looks up the port for the given network and service.
236	// LookupCNAME returns the canonical name for the given host.
239	// the canonical name as part of the lookup.
242	// or more CNAME records.
250	// LookupCNAME returns the canonical name for the given host.
253	// the canonical name as part of the lookup.
256	// or more CNAME records.
265	// protocol, and domain name. The proto is "tcp" or "udp".
267	// by weight within a priority.
269	// LookupSRV constructs the DNS name to look up following RFC 2782.
272	// and proto are empty strings, LookupSRV looks up name directly.
278	// protocol, and domain name. The proto is "tcp" or "udp".
280	// by weight within a priority.
282	// LookupSRV constructs the DNS name to look up following RFC 2782.
285	// and proto are empty strings, LookupSRV looks up name directly.
290	// LookupMX returns the DNS MX records for the given domain name sorted by preference.
295	// LookupMX returns the DNS MX records for the given domain name sorted by preference.
300	// LookupNS returns the DNS NS records for the given domain name.
305	// LookupNS returns the DNS NS records for the given domain name.
310	// LookupTXT returns the DNS TXT records for the given domain name.
315	// LookupTXT returns the DNS TXT records for the given domain name.
321	// of names mapping to that address.
324	// returned. To bypass the host resolver, use a custom Resolver.
330	// of names mapping to that address.


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Counting On news emerged about Jana Duggar soon after her brother Josh was convicted on charges of child pornography. Reports revealed that she endangered a child and pleaded not guilty. However, that changed later, and apparently, she did plead guilty. Did she pay her fine yet?
Counting On Sister Stood Up For Jana Duggar
Fans actually felt a bit sorry for Jana, who seems to be little more than a surrogate mother to her younger siblings. In fact, Katie Joy of Without A Crystal Ball suggested that it might have been one of Anna's kids that wandered off. However, that's never yet been proven. Jessa Seewald noted that it was "an innocent mistake." After a lot of speculation, Jana opened up about it.
Will The Duggar Family Lose Their Assets Because Of Josh's Fines?
Counting On fans saw that Jana Duggar took a while to respond to the story. However, she finally opened up about it. She confirmed on social media that a child in her care wandered off. Contrary to some reports, she said that she was given a citation and did not actually get arrested. Jana added, "They recognized it was a case of a child slipping out of the house when you turn your back for a moment."
Counting On Alum Fined For Child Endangerment
According to The Ashley's Reality Roundup, Jana Duggar pleaded guilty. That came in December. They noted that apparently she initially pleaded not guilty and faced a hearing in court on January 10. The outlet also noted that as she pleaded guilty she needs to pay a fine of $890. Additionally, it was due for immediate payment.
Jana Duggar / Instagram
It's not clear why the citation suggested that the Counting On star had to pay the fine straight away. However, it could possibly mean she previously didn't pay citation fines on time, the outlet speculated.
Fine Apparently Not Paid By Due Date
The Ashley reported that "Jana was fined $890 for the crime on December 15." However, when the article came out on January 4, records showed that she was "listed as "delinquent" due to non-payment."
Counting On fans heard that the child didn't just wander out the yard, it also traveled some distance. Apparently, it was found more than half a mile away. Nevertheless, that doesn't make most fans hate Jana Duggar. For years now, they complained because Michelle and Jim Bob basically used their girls as nannies.
Fans reason that if the child was one belonging to someone who was a parent, they should have been minding it. Poor Jana took that fall and fans absolutely know that she is actually childless and single.
Be sure to catch up on everything happening with GH right now. Come back here often for more Counting On spoilers, news, and updates.
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[…] Counting On Rumors: Jana Duggar Hasn't Paid Fine For Child Endangerment […]


NEW DELHI, April 12 (AGENCIES): Dubbing Union minister Smriti Irani a "serial liar", the Congress on Friday alleged that she has "falsified records" of her educational qualifications and submitted contradictory affidavits to the Election Commission for which she should be disqualified.
In a stinging attack on Irani, Congress spokesperson Priyanka Chaturvedi, at a press conference, also sang the title track of the popular soap opera 'Kyuki saas bhi Kabhi bahu thi' by changing the lyrics to "qualification ke roop badalte hain…(qualification keeps changing)".
"We have no issue with the fact that Smriti Irani is not a graduate. The issue here is that she has lied repeatedly on oath and to the courts. People of India will see through these lies and give a befitting political reply to a 'serial liar'!" she said.
It is clear that she has not only "falsified records" but has submitted contradictory affidavits to the EC for which she is guilty of offence under Section 125A read with Section 33 of the Representation of Peoples Act, Chaturvedi said.
"She is also guilty of hiding information in the Delhi University and misusing her influence and power as a Union Minister. Constitutionally, legally and morally, it would be best if she steps down as the Union minister and by publicly accepting these lies," Chaturvedi said.
She also listed the various affidavits submitted by the BJP leader since 2004 to highlight the "contradiction and falsification of educational qualifications".
Chaturvedi said Irani was also quoted at a media event in August 2014 that she has a degree from the prestigious Yale University in the US. In her affidavit filed on Thursday during her nomination, Irani, who is contesting against Congress chief Rahul Gandhi, said she passed secondary school examination in 1991 and senior secondary school examination in 1993.
She has said she did not complete her Bachelor of Commerce (Part-I) – a three-year degree course – from Delhi University's School of Open Learning in 1994. The party also issued a statement that began with the mock lyrics "qualification ke roop badalte hain…kyunki mantri bhi kabhi graduate thi".
Irani, an actor-turned-politician, had essayed the lead role in 'Kyuki saas bhi Kabhi bahu thi' (Because a mother-in-law was once a daughter-in-law too).