CONFIRMED: Wonder Woman is queer
The character has "obviously" been in love with other women, a DC Comics writer confirmed.

It’s official: Your favorite Amazonian superheroine is into ladies. 

In an interview with Comicosity, Wonder Woman writer Greg Rucka confirmed that the title character has been in love with women, but makes a note to say that it’s not just because of the all-woman island where she lives. Here’s a lengthy but important excerpt in which he says:

This is inherently the problem with Diana: we’ve had a long history of people — for a variety of reasons, including sometimes pure titillation, which I think is the worst reason — say, “Ooo. Look. It’s the Amazons. They’re gay!”

And when you start to think about giving the concept of Themyscira its due, the answer is, “How can they not all be in same sex relationships?” Right? It makes no logical sense otherwise.

It’s supposed to be paradise. You’re supposed to be able to live happily. You’re supposed to be able — in a context where one can live happily, and part of what an individual needs for that happiness is to have a partner — to have a fulfilling, romantic and sexual relationship. And the only options are women.

But an Amazon doesn’t look at another Amazon and say, “You’re gay.” They don’t. The concept doesn’t exist.

Now, are we saying Diana has been in love and had relationships with other women? As Nicola and I approach it, the answer is obviously yes.

And it needs to be yes for a number of reasons. But perhaps foremost among them is, if no, then she leaves paradise only because of a potential romantic relationship with Steve [Trevor]. And that diminishes her character. It would hurt the character and take away her heroism.

When we talk about agency of characters in 2016, Diana deciding to leave her home forever — which is what she believes she’s doing — if she does that because she’s fallen for a guy, I believe that diminishes her heroism.

She doesn’t leave because of Steve. She leaves because she wants to see the world and somebody must go and do this thing. And she has resolved it must be her to make this sacrifice.

So, it’s a thorny question. And I understand as best as I can the desire to see representation on the page. I don’t object to that at all. But my job first and foremost is always to serve the characters as best I can.

For me, all other questions aside — and there are many legitimate reasons to ask the question — the answer first and foremost must be yes, because otherwise it takes away from Diana’s heroism.

That is one hell of an explanation. What do you think?

The Mini Famicom’s controllers are really smol too ⊟ 

We’re typically in support of all things tiny here for obvious reasons, but playing with these Nintendo Classic Mini Family Computer controllers does not look comfortable, lest you have wee, Donald Trump-sized hands.

Maybe when the Japan-only console releases in November, few people will complain about the cute hard-wired controllers (like with the Game Boy Micro), but they are looking 2smol4me to preorder right now.


God sees possibilities in our impossibilities – For with God nothing is impossible.(Luke 1:37)Too often we acknowledge that God can do anything, then we add: “But I just don’t think He’ll do for me!” The reason we give up believing in miracles is that we seem to think we’re not important enough for God to touch us in this way and that we are overlooked by God, that miracles don’t happen to us. We’re too ordinary. God takes ordinary people and makes extraordinary things happen! He chose Mary a simple, humble Jewish girl and chose her to be the mother of our Lord! Dare to believe God can turn your impossible to possible!

I was talking about this with someone earlier this week but like. The interesting thing about where tumblr is at right now is that for those of us who are in our 20s…there’s a reason we’re here. there are more reason NOT to have a tumblr than to have one and I feel like everyone who’s still blogging after age 21 kind of is using it as a crutch or because some aspect of their real life isn’t super satisfying…and that’s okay. but it helps to have that perspective because I think otherwise there’s a lot of nastiness that comes with the realization that we’re not fully formed people yet 

I know it has it’s flaws but bury me with All Things

“Time passes in moments… moments which, rushing past, define the path of a life, just as surely as they lead towards its end. How rarely do we stop to examine that path, to see the reasons why all things happen, to consider whether the path we take in life is our own making, or simply one into which we drift with eyes closed. But what if we could stop, pause to take stock of each precious moment before it passes? Might we then see the endless forks in the road that have shaped a life? And, seeing those choices, choose another path?”

- congratulations on the sex.

- Mulder’s little dance as he listens to music in the basement.

- Scully completely ignoring his crop circle slide show.

- Mulder powering on with his rambles despite knowing Scully has zero interest.

- Mulder pouting.

- Gillian Anderson being an acting powerhouse - Scully’s anguish is written all over her face, the way she walks, carries herself. It’s a relatable 30 something dilemma. Has she made the right choices in her life? Is she happy? Does she wish things were different? 

- “I don’t know what I have.”

- Lesbian couple. Yay!

- Slow mo walking scene complete with Moby. Beautiful, beautiful, beautiful.

- Shoeless, sleepy Scully.

- The hair tuck, the blanket, the pan to the fish tank. My heart.

- “What if there was only one choice and all the other ones were wrong? And there were signs along the way to pay attention to.”

I’m now off to listen to Moby, ponder my own life and read All Things fics.

Oh also. I’m pretty sure no one is saying that Gabriel being a good person with good intentions for most of his life, is excusing any shitty choices he made towards the end. 

bc it is possible to explain that the feelings behind those choices were most likely justifiable, even if the actions were not.

anonymous asked:

I get so fucking sad and tired and numb everytime when I'm trying to explain what it's like, and people say 'oh that happens to me too' or 'i think thats normal though', but I can't find the words to describe why it upsets me so much.

It upsets you because it invalidates your experiences and your struggles. If everyone else experience what you’re experiencing, then there’s no reason why you shouldn’t be able to do what everyone else does - and thus there’s no reason why you shouldn’t be blamed and shamed for not doing it. This attitude is very harmful to disabled people because it’s so important to acknowledge that yes, we have problems and experiences that most people don’t have, that there are actual reasons why we struggle with certain things, that it isn’t just about attitude and not trying hard enough.

anonymous asked:

Years ago congress made it illegal for the CDC to research gun violence or fund studies on it... that's a big reason we don't have "clear evidence" that guns are the problem

CDC isn’t banned from studying gun violence; it’s just too scared to do its job

There’s a common misconception that the Centers for Disease Control and Prevention is forbidden from studying gun violence. That’s simply not true. A number of murky rules have left the agency unclear as to what it is and isn’t allowed to research when it comes to guns. But an outright ban? No.

So why doesn’t the CDC study gun violence? To answer that, let’s take a quick look at its history of gun research.

From 1986 to 1996, the CDC sponsored and carried out public health research on gun violence. In 1993, it funded a study by researchers at the University of Tennessee. The researchers found that “rather than confer protection, guns kept in the home are associated with an increase in the risk of homicide by a family member or intimate acquaintance.

The National Rifle Association didn’t like that. It lobbied to get rid of the CDC’s Center for National Injury Prevention, and while that didn’t happen, it was successful in another sense. In 1996, Congress added a few lines to the Omnibus Appropriations Bill. They said: “None of the funds made available for injury prevention and control at the Centers for Disease Control and Prevention may be used to advocate or promote gun control.“ 

— edit: found something else that is also interesting

Why the CDC still isn’t researching gun violence, despite the ban being lifted two years ago 

Volunteering in the Fandom

I’m going to share some real life experience here because maybe it will help offer perspective. I work for a non-profit. What you may already know is that non-profits rely heavily on volunteers–they could not function properly without a large, active volunteer base. A large part of any non-profit’s focus is on their volunteers for precisely that reason. How do we attract volunteers? How do we keep volunteers? How can we encourage volunteers to not only stay with us, but to talk about us with their friends?

Volunteers do not get paid. They give their time to causes they feel passionate about without expecting any monetary compensation. The best they can really hope for–and the best that many non-profits are able to offer them– is a thank you. “Thank you for coming in today.” “Thank you for giving us your time and energy.” “You are amazing! We couldn’t do this without you.”

A good non-profit is diligent about thanking its volunteers because it knows that if volunteers don’t feel appreciated–if they feel as though the non-profit doesn’t care about the effort they are putting in to this cause that they believe in–they’ll leave.

Volunteer hours are huge for a non-profit for several different reasons. 1) Volunteer hours show how involved your community is in your non-profit. 2) Showing/Logging volunteer hours can be a required portion of some grants (the major source of funding for most non-profits). 3) Showing volunteer hours (and their value) and how they impact your overhead costs is important to potential donors. They like to see that their donations will be used thoughtfully and responsibly.

Nobody thinks that volunteers are crazy for expecting to be thanked for their time. Nobody says “Well, if you really believe in this cause you’ll just volunteer your time anyway. You shouldn’t expect anyone to notice that you’re here Monday, Wednesday, and Friday for 5 hours each day.”

In addition, non-profits have to be very careful about their expectations from their volunteers. You can’t expect someone that is volunteering a few hours a week to perform at the same level as someone that does that job for a living and gets paid to do that job for a living. Volunteers are not obligated to come in at all–they may call in and let you know that they won’t be in for the month because they have family coming, vacation, work is hectic, or they’re ill. As a non-profit, we really can’t get mad at them for that. Yes, it’s inconvenient. Yes, it impacts our non-profit, but, again, they are a volunteer. They are giving you their time. So you smile, and you thank them for everything they’ve already done, and you say that you hope to see them soon.

The advantage that non-profits have over a fandom is that there is a central figure that can issue policies and expectations. The volunteer manager can sit down with the volunteer and make sure that all parties understand what their individual roles are. They volunteer manager can match a volunteer’s skill set to the tasks that need to be done. The volunteer manager can help the volunteer by making sure that they don’t do too much at once, can support the volunteer if they become burned out and need to take a break from volunteering, etc, can check in on them and let them know that the non-profit still thinks about them and that their presence is missed.

The one thing I’d like people to take away is this–aren’t we lucky? We have all of these wonderful, amazing people who are willing to give their time (and occasionally their own money) to support the fandoms to which we each belong. The people that run contests, or websites, or manage groups are all volunteers. The fanfic writers are all volunteers. The people that sponsor or run groups to encourage and support fanfic writers are all volunteers. These people have all donated their blood, sweat, and tears because they believe in our fandom. Wow. Thank you, fandom volunteers. We couldn’t do this without you.

Open Sourcing a Deep Learning Solution for Detecting NSFW Images

By Jay Mahadeokar and Gerry Pesavento

Automatically identifying that an image is not suitable/safe for work (NSFW), including offensive and adult images, is an important problem which researchers have been trying to tackle for decades. Since images and user-generated content dominate the Internet today, filtering NSFW images becomes an essential component of Web and mobile applications. With the evolution of computer vision, improved training data, and deep learning algorithms, computers are now able to automatically classify NSFW image content with greater precision.

Defining NSFW material is subjective and the task of identifying these images is non-trivial. Moreover, what may be objectionable in one context can be suitable in another. For this reason, the model we describe below focuses only on one type of NSFW content: pornographic images. The identification of NSFW sketches, cartoons, text, images of graphic violence, or other types of unsuitable content is not addressed with this model.

To the best of our knowledge, there is no open source model or algorithm for identifying NSFW images. In the spirit of collaboration and with the hope of advancing this endeavor, we are releasing our deep learning model that will allow developers to experiment with a classifier for NSFW detection, and provide feedback to us on ways to improve the classifier.

Our general purpose Caffe deep neural network model (Github code) takes an image as input and outputs a probability (i.e a score between 0-1) which can be used to detect and filter NSFW images. Developers can use this score to filter images below a certain suitable threshold based on a ROC curve for specific use-cases, or use this signal to rank images in search results.

Convolutional Neural Network (CNN) architectures and tradeoffs

In recent years, CNNs have become very successful in image classification problems [1] [5] [6]. Since 2012, new CNN architectures have continuously improved the accuracy of the standard ImageNet classification challenge. Some of the major breakthroughs include AlexNet (2012) [6], GoogLeNet [5], VGG (2013) [2] and Residual Networks (2015) [1]. These networks have different tradeoffs in terms of runtime, memory requirements, and accuracy. The main indicators for runtime and memory requirements are:

  1. Flops or connections – The number of connections in a neural network determine the number of compute operations during a forward pass, which is proportional to the runtime of the network while classifying an image.
  2. Parameters -–The number of parameters in a neural network determine the amount of memory needed to load the network.

Ideally we want a network with minimum flops and minimum parameters, which would achieve maximum accuracy.

Training a deep neural network for NSFW classification

We train the models using a dataset of positive (i.e. NSFW) images and negative (i.e. SFW – suitable/safe for work) images. We are not releasing the training images or other details due to the nature of the data, but instead we open source the output model which can be used for classification by a developer.

We use the Caffe deep learning library and CaffeOnSpark; the latter is a powerful open source framework for distributed learning that brings Caffe deep learning to Hadoop and Spark clusters for training models (Big shout out to Yahoo’s CaffeOnSpark team!).

While training, the images were resized to 256x256 pixels, horizontally flipped for data augmentation, and randomly cropped to 224x224 pixels, and were then fed to the network. For training residual networks, we used scale augmentation as described in the ResNet paper [1], to avoid overfitting. We evaluated various architectures to experiment with tradeoffs of runtime vs accuracy.

  1. MS_CTC [4] – This architecture was proposed in Microsoft’s constrained time cost paper. It improves on top of AlexNet in terms of speed and accuracy maintaining a combination of convolutional and fully-connected layers.
  2. Squeezenet [3] – This architecture introduces the fire module which contain layers to squeeze and then expand the input data blob. This helps to save the number of parameters keeping the Imagenet accuracy as good as AlexNet, while the memory requirement is only 6MB.
  3. VGG [2] – This architecture has 13 conv layers and 3 FC layers.
  4. GoogLeNet [5] – GoogLeNet introduces inception modules and has 20 convolutional layer stages. It also uses hanging loss functions in intermediate layers to tackle the problem of diminishing gradients for deep networks.
  5. ResNet-50 [1] – ResNets use shortcut connections to solve the problem of diminishing gradients. We used the 50-layer residual network released by the authors.
  6. ResNet-50-thin – The model was generated using our pynetbuilder tool and replicates the Residual Network paper’s 50-layer network (with half number of filters in each layer). You can find more details on how the model was generated and trained here.

Tradeoffs of different architectures: accuracy vs number of flops vs number of params in network.

The deep models were first pre-trained on the ImageNet 1000 class dataset. For each network, we replace the last layer (FC1000) with a 2-node fully-connected layer. Then we fine-tune the weights on the NSFW dataset. Note that we keep the learning rate multiplier for the last FC layer 5 times the multiplier of other layers, which are being fine-tuned. We also tune the hyper parameters (step size, base learning rate) to optimize the performance.

We observe that the performance of the models on NSFW classification tasks is related to the performance of the pre-trained model on ImageNet classification tasks, so if we have a better pretrained model, it helps in fine-tuned classification tasks. The graph below shows the relative performance on our held-out NSFW evaluation set. Please note that the false positive rate (FPR) at a fixed false negative rate (FNR) shown in the graph is specific to our evaluation dataset, and is shown here for illustrative purposes. To use the models for NSFW filtering, we suggest that you plot the ROC curve using your dataset and pick a suitable threshold.

Comparison of performance of models on Imagenet and their counterparts fine-tuned on NSFW dataset.

We are releasing the thin ResNet 50 model, since it provides good tradeoff in terms of accuracy, and the model is lightweight in terms of runtime (takes < 0.5 sec on CPU) and memory (~23 MB). Please refer our git repository for instructions and usage of our model. We encourage developers to try the model for their NSFW filtering use cases. For any questions or feedback about performance of model, we encourage creating a issue and we will respond ASAP.

Results can be improved by fine-tuning the model for your dataset or use case. If you achieve improved performance or you have trained a NSFW model with different architecture, we encourage contributing to the model or sharing the link on our description page.

Disclaimer: The definition of NSFW is subjective and contextual. This model is a general purpose reference model, which can be used for the preliminary filtering of pornographic images. We do not provide guarantees of accuracy of output, rather we make this available for developers to explore and enhance as an open source project.

We would like to thank Sachin Farfade, Amar Ramesh Kamat, Armin Kappeler, and Shraddha Advani for their contributions in this work.


[1] He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. “Deep residual learning for image recognition” arXiv preprint arXiv:1512.03385 (2015).

[2] Simonyan, Karen, and Andrew Zisserman. “Very deep convolutional networks for large-scale image recognition.”; arXiv preprint arXiv:1409.1556(2014).

[3] Iandola, Forrest N., Matthew W. Moskewicz, Khalid Ashraf, Song Han, William J. Dally, and Kurt Keutzer. “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and 1MB model size.”; arXiv preprint arXiv:1602.07360 (2016).

[4] He, Kaiming, and Jian Sun. “Convolutional neural networks at constrained time cost.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5353-5360. 2015.

[5] Szegedy, Christian, Wei Liu, Yangqing Jia, Pierre Sermanet,Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. “Going deeper with convolutions” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-9. 2015.

[6] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. “Imagenet classification with deep convolutional neural networks” In Advances in neural information processing systems, pp. 1097-1105. 2012.



I think that at the end Nero didn’t kill Avilio/Angelo.

I think the ending is left to the imagination of everyone who watched 91d
We see Nero and Avilio on the road again, like before.
It started a bit darker but then the mood started changing, them ‘stealing the car’, a very moving moment, we see Avilio horrendous driving, Nero freeing him, Nero going where he wants… AND THEN
The beach scene.

Nero saying: you just live, you don’t need a reason ( who said after Avilio killed Corteo ‘I’ll give him a reason to live’?)
Avilio admitting he didn’t want to kill Nero.

In ep 1 we see a dead Avilio who finds a new reason to live and has a goal and he kinda forces corteo to help him and uses him ( I think he still didn’t want to hurt or get him killed, but…). They meet Nero and Av kills his first target. Then they are on the road, they get to know each other, have fun and they save each other. They go back and Avilio becomes like a right hand to Nero, he trusts him. We already start to see corteo asking why didn’t Avilio already kill Nero and he makes the excuse of saying it’s because he will use him to get close to don vanetti.
I think Nero kinda started liking his life, working for (with) Nero and having corteo as a brother figure. Then corte goes and fucks up and Avilio has to make a move and decide. After he kills corteo he’s like an empty shell, a broken person, but after the playhouse he fells even emptier and realise revenge didn’t really do him any good.

Going back to the beach scene, I feel like it’s a repeat of the past:
We see Nero firing his gun and missing little Angelo.
Avilio walks in front of Nero
Nero fires.

It’s like everything is different but at the same time it’s all the same.
Avilio walks slower.
They know each other.
They understand each other.
They’ve grown.

But I think at the end Nero didn’t kill Avilio, he missed.
I mean why woul he smile at the end.
And the footprints…

O well to everyone their own!!

When will people stop assuming that everyone who likes Jasper hates and/or ignores Bismuth??? Like wtf we love her we just hate that you keep using her as an excuse to guilt trip us and put us down

It’s somehow unreal to these people that we could somehow like or enjoy more than one character with a large body type? (Not to mention the people who for some reason think that we only care about Jasper because she’s white or white passing, which she is ABSOLUTELY not…)

Like you can say “why doesn’t Bismuth get more love” without putting down/attacking people who happen to like other characters, but a lot of these people don’t seem to care about Bismuth unless they’re using her as an excuse to be an asshole to Jasper fans anyway, so yanno.