The show exists in a strange world where men repeatedly confess their love for each other. Does it make them better people? Much ink has been spilled, and countless TikToks recorded, in an effort to explain the female fervor unleashed by the series " Heated Rivalry ." I, a thirty-eight-year-old woman who owns a T-shirt that bears the logo of Shane Hollander's Montreal Metros and another that celebrates Ilya Rozanov's Boston Raiders (Valentine's Day gifts, it should be said, from my indulgent husband), don't find its appeal so mystifying. Two gorgeous young men, as elegantly muscled as Myron's discus thrower, have ecstatically unbridled, mutually satisfying sex to a soundtrack designed to tickle elder millennials' nostalgia-pleasure centers, all while falling in the kind of soul-sustaining love that most of us can only dream of.

This research investigates the efficiency of Floyd algorithm for obstacle-free path planning for autonomous aerial vehicles (UAVs) or drones. Floyd algorithm is used to generate the shortest paths for UAVs to fly from any place to the destination in a large-scale field with obstacles which UAVs cannot fly over. The simulation results demonstrated that Floyd algorithm effectively plans the shortest obstacle-free paths for UAVs to fly to a destination. It is verified that Floyd algorithm holds a time complexity of O(n3). This research revealed a correlation of a cubic polynomial relationship between the time cost and the size of the field, no correlation between the time cost and the number of obstacles, and no correlation between the time cost and the number of UAVs in the tested field. The applications of the research results are discussed in the paper as well.

Text-to-image generative models offer many innovative services but also raise ethical concerns due to their potential to generate unethical images. Most publicly available text-to-image models employ safety filters to prevent unintended generation intents. In this work, we introduce the Divide-and-Conquer Attack to circumvent the safety filters of state-of-the-art text-to-image models. Our attack leverages LLMs as agents for text transformation, creating adversarial prompts from sensitive ones. We have developed effective helper prompts that enable LLMs to break down sensitive drawing prompts into multiple harmless descriptions, allowing them to bypass safety filters while still generating sensitive images. This means that the latent harmful meaning only becomes apparent when all individual elements are drawn together. Our evaluation demonstrates that our attack successfully circumvents the closed-box safety filter of SOTA DALLE-3 integrated natively into ChatGPT to generate unethical images. This approach, which essentially uses LLM-generated adversarial prompts against GPT-4-assisted DALLE-3, is akin to using one's own spear to breach their shield. It could have more severe security implications than previous manual crafting or iterative model querying methods, and we hope it stimulates more attention towards similar efforts. Our code and data are available at: https://github.com/researchcode001/Divide-and-Conquer-Attack

The phrase "mission creep" entered the popular discourse in the early to mid-1990s. The trick is to do this without inviting what a senior official called "mission creep" -- the expansion of the role to include, for example, raiding neighborhoods controlled by General Aidid and searching for weapons. Like countless military and sports terms before it, we now understand it in a broader context. It's one of those phrases that perfectly encapsulates a commonly understood experience -- projects whose size, scope and focus shift so gradually you hardly even notice. I bring this up in the context of an op-ed the Electronic Frontier Foundation published last year.

The addition of a robot to a team can be difficult if the human teammates do not trust the robot. This can result in underutilization or disuse of the robot, even if the robot has skills or abilities that are necessary to achieve team goals or reduce risk. To help a robot integrate itself with a human team, we present an agent algorithm that allows a robot to estimate its trustworthiness and adapt its behavior accordingly. As behavior adaptation is performed, using case-based reasoning (CBR), information about the adaptation process is stored and used to improve the efficiency of future adaptations.

Robots can be added to human teams to provide improved capabilities or to perform tasks that humans are unsuited for. However, in order to get the full benefit of the robots the human teammates must use the robots in the appropriate situations. If the humans do not trust the robots, they may underutilize them or disuse them which could result in a failure to achieve team goals. We present a robot that is able to estimate its trustworthiness and adapt its behavior accordingly. This technique helps the robot remain trustworthy even when changes in context, task or teammates are possible.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A police union in Miami shared a year-old photo of Chief Art Acevedo appearing to flash the same "OK" hand gesture he suspended an officer for using last week amid allegations it represented "White power." "'Do as I say not as I do' I guess it [is] what we should get used to from now on," Miami Fraternal Order of Police Lodge 20 tweeted, sharing an image of Acevedo when he was still Houston's police chief. The photo shows Acevedo during a visit to Jack Yates High School in Houston last summer.

In one 2020 complaint to the Consumer Financial Protection Bureau, a consumer echoed the words of George Floyd to describe an experience with a financial company, saying "you all will not let me breathe." The consumer wanted to know why the firm would "not take their knee off ... my neck?" Another criticized a company for its approach to sexual identity issues. "The employees refused to be sensitive to my pronouns' and name change," the consumer said. "As a result, my account was closed after years of torture from this credit card company."

The first nine months of 2020 have felt like a lifetime and like no time at all. In one quick moment, everything important became mediated through screens: the joy of weddings, the sorrow of funerals, conversations with our closest friends. Meanwhile, mom-and-pop stores shuttered, and big tech companies announced record profits. Technologies are lifelines connecting us to one another; technologies are increasingly pulling us apart. Through the pandemic and the protests for racial justice, 2020 has made it ever more clear how critical it is to consider the public purpose and implications of the technologies we use.

In an age of very little institutional trust, without a firm historical context that future historians and the public can rely on to authenticate digital media events of the past, we may be looking at the dawn of a new era of civilization: post-history. We need to act now to ensure the continuity of history without stifling the creative potential of these new AI tools. Imagine that it's the year 2030. You load Facebook on your smartphone, and you're confronted with a video that shows you drunk and deranged, sitting in your living room saying racist things while waving a gun. Typical AI-assisted character attack, you think. There's a 1970s interview video of Neil Armstrong and Buzz Aldrin on The Dick Cavett Show declaring, "We never made it to the moon.