Personal Assistant Systems
The World's Top Consumers Cause Up to 5.7 Trillion in Environmental Damage Every Year
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A Millennium of Pressure Has Been Building Under California Fault Lines. What Does That Mean for Earthquake Risk?
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Hegseth Announces Review of U.S. Troops in Europe and Scolds NATO Allies Over 'Shameful' Response to Iran War
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9 Questions Dads Wish Their Kids Would Ask Them
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Australia and the U.S. May Be Allies, But Expect a World Cup Tussle
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Greta Lee Plays a Tablet in Toy Story 5. In Real Life, She Bought Her Kids a Landline
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Adobe brings its Firefly AI Assistant inside of Premiere, Photoshop and Illustrator
The company is also previewing an upgraded creative AI studio experience. Earlier this year, Adobe debuted Firefly AI Assistant, an AI agent that could work across its family of Creative Cloud apps to complete multi-step workflows on behalf of users. Today, the company is previewing an updated Firefly creative AI studio experience that expands the capabilities of that software, starting with an upgrade to AI Assistant's ability to carry context forward. A new Elements feature allows users to save characters, locations and objects they've previously generated to reuse in future outputs. Adobe suggests this capability will allow AI Assistant to better maintain consistency across stories, campaigns and projects that evolve inside of Firefly.
Trump's Iran Agreement Draws More Alarm Than Relief From GOP
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Iranians Welcome a Peace Deal, but Worry About What Comes Next
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Generalized Top-k Mallows Model for Ranked Choices
The classic Mallows model is a foundational tool for modeling user preferences. However, it has limitations in capturing real-world scenarios, where users often focus only on a limited set of preferred items and are indifferent to the rest. To address this, extensions such as the top-k Mallows model have been proposed, aligning better with practical applications. In this paper, we address several challenges related to the generalized top-k Mallows model, with a focus on analyzing buyer choices. Our key contributions are: (1) a novel sampling scheme tailored to generalized top-k Mallows models, (2) an efficient algorithm for computing choice probabilities under this model, and (3) an active learning algorithm for estimating the model parameters from observed choice data. These contributions provide new tools for analysis and prediction in critical decision-making scenarios. We present a rigorous mathematical analysis for the performance of our algorithms. Furthermore, through extensive experiments on synthetic data and real-world data, we demonstrate the scalability and accuracy of our proposed methods, and we compare the predictive power of Mallows model for top-k lists compared to the simpler Multinomial Logit model.