OSU < Minnesota and OSU isn't even that good.
The Best Nursing Scholarships For Men | NurseJournal.org
Discover the best scholarships for male nurses. Explore opportunities to fund your nursing education and advance your career.nursejournal.org
Funny events in anti-woke world
- Thread starter Agema
- Start date
Wut?
I'll be more clear then; show me just in the world of sports how the world hates men and male-only groups, and empowers women-only groups.
There isn't since society as of right now deems sports for men > women. But in tech and finance, affirmative action for nonmale groups who are white or Asian is everywhere. I literally couldn't even find an Asian-only career group or affirmative action program in any S&P500 company. The US government allegory has one for Asian-Americans but there is a huge backlog due to not enough HR, plus if you come from certain countries(China) your basically undergoing a heavier check for obvious reasons.
You are not discriminated against in tech and finance for being male or Asian.
Neither are women, and African Americans, and Latinos but they get programs to help them.
Yeah, what you're talking about here has nothing to do with companies like SBI and isn't something they have any power over. What you're complaining about is pure, simple greed which has always been an issue with game companies, long before they embraced "wokeness".
It's Dwarvenhobble, he's always made bullshit stances and try to blame things on "wokeness" and "forced diversity", while enabling, glorifying, or ignoring the shit on the far right, or ignore corporate greed so long as they're "apolitical/neutral" and targeting those he hates. Sad and pathetic, but I wouldn't waste pity on people like him.
Which is why politics, finance, industry, the military, religious groups etc are all male dominated?
That's only for high-performing men. Most men aren't in the top 10%.
Edit: Also religious groups are for scammers and suckers.
Ladies and gentlemen of the jury, Exhibit A.
https://www.thegamer.com/gamers-complain-grand-theft-auto-6-woke/
This Predator killed an entire tracking party. It's not like subsequent films have the Predators fighting elite soldiers all the time. The second film had it taking on L.A. cops and gang members. I don't even know what the hell you mean by not really having her fight but that's clearly nonsense and using the tired "girlboss" criticism for any female character who is remotely competent is enough invalidate your argument.
The end of her show establishes that mysticism is not the source of her powers. And let's not pretend right wingers only started having an issue with this character after she got her MCU show.
Virtually every character in the MCU was changed. Kingpin and Echo got the least amount of divergences from their comic counterparts.
The people who actually like MCU Ghost aren't the one who believe everyone in entertainment media is out to subjugate, humiliate or replace straight white men just because women, minorities and LGBT people are being more normalized as main leads in fiction. They are not the ones crying about white or redhead erasure when a non-white actor plays a white character. They are not the ones who yell about puritanism, communism, Sharia Law or any other fancy term the anti-woke crowd doesn't understand when they see a woman that doesn't give them an erection. And they're not the ones organizing social media groups dedicated to "warning" people about a consultancy firm that they think has the video game industry in such a death grip that they can force studios into including diversity in their games.
Barbie, Captain Marvel, the Black Panther movies, the Star Wars sequels, the Resident Evil remakes, Street Fighter 6, the last four Mortal Kombat games and the Lara Croft reboot were all hits. That hasn't stopped the anti-woke brigade from shouting "go woke, go broke" as if they can change reality the more they repeat it.
You don't think it's a teensy bit odd that, for a society you believe hates men, it's men who run it?
I mean, obviously you don't, the logical disconnect is something you'd dismiss, downplay or ignore. But, c'mon, that's worse than your habit of using AI generated answers as a source.
They hate most men. The ones they don't hate were never going to be hated to begin with in any society.
Also as for AI...
Also you guys complain about AI and how it won't replace jobs. Here's data analysis with python.
import requests
import torch
import torch.nn as nn
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
# Fetch historical price data for Shiba Inu coin from CoinGecko API
def fetch_shiba_inu_data():
url = 'https://api.coingecko.com/api/v3/coins/shiba-inu/market_chart?vs_currency=usd&days=max'
response = requests.get(url)
data = response.json()
prices = data['prices']
df = pd.DataFrame(prices, columns=['Timestamp', 'Price'])
df['Timestamp'] = pd.to_datetime(df['Timestamp'], unit='ms')
df.set_index('Timestamp', inplace=True)
return df
# Fetch the data
data = fetch_shiba_inu_data()
# Preprocess the data and prepare it for training
scaler = MinMaxScaler(feature_range=(-1, 1))
prices = data['Price'].values.reshape(-1, 1)
scaled_prices = scaler.fit_transform(prices)
def create_dataset(dataset, look_back=1):
X, Y = [], []
for i in range(len(dataset) - look_back):
a = dataset[i
i + look_back), 0]
X.append(a)
Y.append(dataset[i + look_back, 0])
return np.array(X), np.array(Y)
look_back = 10
X, Y = create_dataset(scaled_prices, look_back)
# Split the data into training and testing sets
train_size = int(len(X) * 0.8)
X_train, Y_train = X[:train_size], Y[:train_size]
X_test, Y_test = X[train_size:], Y[train_size:]
# Convert the data to PyTorch tensors
X_train = torch.from_numpy(X_train).float()
Y_train = torch.from_numpy(Y_train).float()
X_test = torch.from_numpy(X_test).float()
Y_test = torch.from_numpy(Y_test).float()
# Define the neural network model using PyTorch
class RegressionModel(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(RegressionModel, self).__init__()
self.hidden = nn.Linear(input_size, hidden_size)
self.output = nn.Linear(hidden_size, output_size)
def forward(self, x):
x = torch.relu(self.hidden(x))
x = self.output(x)
return x
input_size = look_back
hidden_size = 64
output_size = 1
model = RegressionModel(input_size, hidden_size, output_size)
# Train the model using the prepared data
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
num_epochs = 100
for epoch in range(num_epochs):
inputs = X_train
targets = Y_train.view(-1, 1)
outputs = model(inputs)
loss = criterion(outputs, targets)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (epoch+1) % 10 == 0:
print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')
# Evaluate the model's performance and make predictions
model.eval()
with torch.no_grad():
inputs = X_test
targets = Y_test.view(-1, 1)
outputs = model(inputs)
loss = criterion(outputs, targets)
print(f'Test Loss: {loss.item():.4f}')
# Make predictions
predicted_prices = scaler.inverse_transform(outputs.numpy())
# Get the dates for the predicted prices
last_date = data.index[-1]
future_dates = pd.date_range(start=last_date, periods=len(predicted_prices)+1, freq='D')[1:]
# Create a DataFrame for the predicted prices
predicted_df = pd.DataFrame({'Date': future_dates, 'Price': predicted_prices.flatten()})
predicted_df.set_index('Date', inplace=True)
# Plot the actual and predicted prices
plt.figure(figsize=(12, 6))
plt.plot(data.index, data['Price'], label='Actual Price')
plt.plot(predicted_df.index, predicted_df['Price'], label='Predicted Price')
plt.xlabel('Date')
plt.ylabel('Price (USD)')
plt.legend()
plt.show()
import requests
import torch
import torch.nn as nn
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
# Fetch historical price data for Shiba Inu coin from CoinGecko API
def fetch_shiba_inu_data():
url = 'https://api.coingecko.com/api/v3/coins/shiba-inu/market_chart?vs_currency=usd&days=max'
response = requests.get(url)
data = response.json()
prices = data['prices']
df = pd.DataFrame(prices, columns=['Timestamp', 'Price'])
df['Timestamp'] = pd.to_datetime(df['Timestamp'], unit='ms')
df.set_index('Timestamp', inplace=True)
return df
# Fetch the data
data = fetch_shiba_inu_data()
# Preprocess the data and prepare it for training
scaler = MinMaxScaler(feature_range=(-1, 1))
prices = data['Price'].values.reshape(-1, 1)
scaled_prices = scaler.fit_transform(prices)
def create_dataset(dataset, look_back=1):
X, Y = [], []
for i in range(len(dataset) - look_back):
a = dataset[i
i + look_back), 0]X.append(a)
Y.append(dataset[i + look_back, 0])
return np.array(X), np.array(Y)
look_back = 10
X, Y = create_dataset(scaled_prices, look_back)
# Split the data into training and testing sets
train_size = int(len(X) * 0.8)
X_train, Y_train = X[:train_size], Y[:train_size]
X_test, Y_test = X[train_size:], Y[train_size:]
# Convert the data to PyTorch tensors
X_train = torch.from_numpy(X_train).float()
Y_train = torch.from_numpy(Y_train).float()
X_test = torch.from_numpy(X_test).float()
Y_test = torch.from_numpy(Y_test).float()
# Define the neural network model using PyTorch
class RegressionModel(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(RegressionModel, self).__init__()
self.hidden = nn.Linear(input_size, hidden_size)
self.output = nn.Linear(hidden_size, output_size)
def forward(self, x):
x = torch.relu(self.hidden(x))
x = self.output(x)
return x
input_size = look_back
hidden_size = 64
output_size = 1
model = RegressionModel(input_size, hidden_size, output_size)
# Train the model using the prepared data
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
num_epochs = 100
for epoch in range(num_epochs):
inputs = X_train
targets = Y_train.view(-1, 1)
outputs = model(inputs)
loss = criterion(outputs, targets)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (epoch+1) % 10 == 0:
print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')
# Evaluate the model's performance and make predictions
model.eval()
with torch.no_grad():
inputs = X_test
targets = Y_test.view(-1, 1)
outputs = model(inputs)
loss = criterion(outputs, targets)
print(f'Test Loss: {loss.item():.4f}')
# Make predictions
predicted_prices = scaler.inverse_transform(outputs.numpy())
# Get the dates for the predicted prices
last_date = data.index[-1]
future_dates = pd.date_range(start=last_date, periods=len(predicted_prices)+1, freq='D')[1:]
# Create a DataFrame for the predicted prices
predicted_df = pd.DataFrame({'Date': future_dates, 'Price': predicted_prices.flatten()})
predicted_df.set_index('Date', inplace=True)
# Plot the actual and predicted prices
plt.figure(figsize=(12, 6))
plt.plot(data.index, data['Price'], label='Actual Price')
plt.plot(predicted_df.index, predicted_df['Price'], label='Predicted Price')
plt.xlabel('Date')
plt.ylabel('Price (USD)')
plt.legend()
plt.show()
Most PEOPLE aren't in the top 10%. If most people were in the top 10%, it wouldn't be the top 10%, would it?
Looking at the list of the 20 richest people in the world, 10% of them are women. Of those two, the richer is 33% owner of L'Oreal, and the other co-owns Wal-Mart.
that’s literally 20 people. And don’t even get me started on outsourcing white collar positions.
No one is preventing studios from using Sweet Baby, merely informing others who are specifically interested in knowing (aka following the curator) which studios do for which titles. I wouldn't call this "refusing to accept the artistic freedoms of studios", especially not compared to things like the "controversy" surrounding Divinity: Original Sin cover art with the exposed midriff (you know, the one involving threats of violence to the studio to demand it be changed).
You'd probably hate my steam curator list - most of them are red flags for certain kinds of DRM or detail cut content between releases, that sort of thing. Suicide Squad: Kill the Justice League (one of the 16 games listed on Sweet Baby Inc Detected) literally shows up on several of my curators for using Denuvo (Denuvo and 3rd party DRM curators), using Easy Anti-Cheat (anti-cheat rootkits curators), and removing negative reviews.
Frankly I'd love a curator that tagged games localized by companies that have a bad habit of making substantial changes to the work beyond a broadly faithful translation (yes, I know you can't just do literal word for word translation and have it be any good but there's a big jump between literal translation, a broadly faithful translation [in other words trying to represent the original meaning and tone rather than word for word translating things like idioms that don't hold the same meaning], and what certain localizers have done [cut content, fuck with authorial intent, insert unrelated political commentary, etc]). Aka "These people tend to be the bad kind of localizers, and you should know they localized this for your region."
So you'd support a curator for "games with problematic DRM" but not one for "games with Denuvo" because the latter unfairly targets Denuvo Software Solutions GmbH?
Sweet Baby Inc is not the only company similar to it out there, would you feel better if someone made a curator for games using it or very similar consultancy firms to much the same effect? Would that change if they listed which such firm the game was using in the notes?
Or how about a hypothetical - let's imagine after seeing the success of Hogwarts Legacy, JKR is inspired to get into game writing. Would a curator singling out games she was involved in in order to avoid supporting her because of her political views be acceptable? What about one listing games known to involve known TERFs, not merely limited to JKR?
Isn't the intent supplied by the user directing the AI tool? Like it feels like the arguments surrounding AI art were probably had in the 19th century regarding photography. A lot of it is people being afraid of a new, more sophisticated tool.
Note that if we assume this to be accurate, the response to finding out that a segment of consumers are uninterested in such games is that people pointing them out need to be silenced. Apparently consumers are supposed to blindly consume and how dare they pay attention to who is involved in development?
He could even argue it's not a campaign donation - he's just posting bond so a friend can appeal his legal case.
Which is totally legal and not at all buying a politician. /s
Social pressure tends to lie on the side of maintaining or expanding women's-only spaces and benefits and ending men's-only spaces and benefits. Regardless of what political side or what sort of space. Hell, this is why the standard formulation of being angry at trans people is "men in dresses getting access to women's spaces" - they barely recognize that trans men exist, and aren't concerned with them having access to things that everyone should be able to access anyways. This is why NCFM suing people in CA under the Unruh Civil Rights Act get treated as somewhere between a joke and a villain (short version is that CA has one of the most thorough laws against sex discrimination in the country, and NCFM pretty routinely sues businesses in CA under it for things like offering different services or pricing that favor women or opportunities only offered to women).
*looks at the criminal justice system and education*
Every professional sports league in the US is either restricted to women only, or technically allows people to try out of any sex if they can compete at a high enough level. Occasionally a woman tries, though they generally don't make the cut. Lauren Silberman tried out for the NFL in 2013, exacerbated an injury she had and had to quit early on, for example. Multiple women have tried out for NBA teams going back to the 70s as well (some thought Britney Griner would do so and would likely have been successful if she had, though less likely to be a superstar on the court when competing against NBA players - she instead chose to be the biggest fish in the smaller pond).
Hell, this same structure even appears in chess, where chess tournaments are either open to people of any sex, or only open to women.
The Best Nursing Scholarships For Men | NurseJournal.org
Discover the best scholarships for male nurses. Explore opportunities to fund your nursing education and advance your career.nursejournal.org
Worth noting that of those only two are specifically for men. The rest are available for persons of any sex. Hell, the page for the first one I clicked on (I think it was the third on the overall list) only has pictures of women on it's web page. By comparison, you should try looking up a list of scholarships for women in engineering and see how many you can find that are only available for or specifically targeted to women.
Maybe we should also look at other things that are male dominated, like the rough sleeping homeless or suicides or victims of criminal violence? That's at least a much larger sample that the very top of the mountain, even though looking at either as a proxy for men as a whole is not reflective of men as a whole.
Sure, so the extent of their artistic contribution is what they feed in, not the sum of what comes out. What comes out owes most of its content and form to mindless data-scraped dross.
With photography, the user is responsible for much more of the form and content of the output-- the entire composition.