How does a user's consent decision influence

You must have heard of Dubai chocolate: the sticky, indulgent confectionary filled with pistachio cream, tahini and shreds of knafeh pastry, which has become a global sensation. Now the decadent bar has inspired South Korea's latest dessert craze. The Dubai chewy cookie has been selling like wildfire - and even restaurants that don't usually offer baked goods are trying to get a nibble of the market. Despite its name, the cookie's texture more closely resembles a rice cake, and is made by stuffing pistachio cream and knafeh shreds into a chocolate marshmallow. Shops are selling hundreds of cookies within minutes and the frenzy has sent prices of key ingredients surging, local media reported.

Cookies are designed to enable more accurate identification and tracking of user behavior, in turn allowing for more personalized ads and better performing ad campaigns. Given the additional information that is recorded, questions related to privacy and fairness naturally arise. How does a user's consent decision influence how much the system can learn about their demographic and tastes? Is the impact of a user's consent decision on the recommender system's ability to learn about their latent attributes uniform across demographics? We investigate these questions in the context of an engagement-driven recommender system using simulation. We empirically demonstrate that when consent rates exhibit demographic-dependence, user consent has a disparate impact on the recommender agent's ability to estimate users' latent attributes. In particular, we find that when consent rates are demographic-dependent, a user disagreeing to share their cookie may counter-intuitively cause the recommender agent to know more about the user than if the user agreed to share their cookie. Furthermore, the gap in base consent rates across demographics serves as an amplifier: users from the lower consent rate demographic who agree to cookie sharing generally experience higher estimation errors than the same users from the higher consent rate demographic, and conversely for users who choose to disagree to cookie sharing, with these differences increasing in consent rate gap. We discuss the need for new notions of fairness that encourage consistency between a user's privacy decisions and the system's ability to estimate their latent attributes.

Understanding a bit of chemistry can transform your baking skills. The perfect cookie is a matter of taste, but these tips and tricks can help you develop your perfect recipe. Breakthroughs, discoveries, and DIY tips sent every weekday. "Cooking is art--but baking is science," Bill Nye the Science Guy once said . While a batch of freshly baked chocolate chip cookies doesn't resemble anything you'd whip up in a chemistry lab (hopefully), there's plenty of chemistry happening in your oven.

Biscotti once fed Roman navies and Christopher Columbus's expeditions Long before it met espresso, this crunchy pastry kept sailors fed. Roman writer Pliny the Elder was the first writer to mention biscotti in 77 CE. Breakthroughs, discoveries, and DIY tips sent every weekday. Step into a typical Italian restaurant in the U.S. and you'll likely find "biscotti" on the menu. Typically served with a glass of sweet wine or cappuccino, these log-shaped crunchy cookies are a beloved treat that most of us associate with cozy dinners and Little Italy.

Food drying is widely used to reduce moisture content, ensure safety, and extend shelf life. Color evolution of food samples is an important indicator of product quality in food drying. Although existing studies have examined color changes under different drying conditions, current approaches primarily rely on low-dimensional color features and cannot fully capture the complex, dynamic color trajectories of food samples. Moreover, existing modeling approaches lack the ability to generalize to unseen process conditions. To address these limitations, we develop a novel multi-modal color-trajectory prediction method that integrates high-dimensional temporal color information with drying process parameters to enable accurate and data-efficient color trajectory prediction. Under unseen drying conditions, the model attains RMSEs of 2.12 for cookie drying and 1.29 for apple drying, reducing errors by over 90% compared with baseline models. These experimental results demonstrate the model's superior accuracy, robustness, and broad applicability. Introduction As a fundamental operation in industrial food processing, drying enables long-term preservation, enhances texture and flavor, and facilitates storage and transportation [1]. However, food drying is a highly complex process [2].

This well-studied problem has plenty of proposed solutions (known as Large Neighborhood Search methods), though.

This is a story about a classroom. The kids in the classroom are all waiting in line to get a snack from their teacher. What are the kids having for snack?

How does a user's consent decision influence