Deliberation plays a crucial role in shaping outcomes by weighing diverse perspectives before reaching decisions. With recent advancements in Natural Language Processing, it has become possible to computationally model deliberation by analyzing opinion shifts and predicting potential outcomes under varying scenarios. In this study, we present a comparative analysis of multiple NLP techniques to evaluate how effectively models interpret deliberative discourse and produce meaningful insights. Opinions from individuals of varied backgrounds were collected to construct a self-sourced dataset that reflects diverse viewpoints. Deliberation was simulated using product presentations enriched with striking facts, which often prompted measurable shifts in audience opinions. We have given comparative analysis between two models namely Frequency-Based Discourse Modulation and Quantum-Deliberation Framework which outperform the existing state of art models. Deliberation is the structured process of reasoning, dialogue, and weighing evidence before decisions are made. Unlike ordinary conversation, it emphasizes logical argumentation, inclusivity, and critical reflection.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. You likely brush your teeth every night -- but you may not realize how important it is to wash your face before going to bed every night, too. To learn more about why face-washing should be on your bedtime to-do list, Fox News Digital asked two dermatologists for the clean truth. When you look in the mirror, you likely don't see the buildup of substances on your face from that day.

Simulating the effects of skincare products on face is a potential new way to communicate the efficacy of skincare products in skin diagnostics and product recommendations. Furthermore, such simulations enable one to anticipate his/her skin conditions and better manage skin health. However, there is a lack of effective simulations today. In this paper, we propose the first simulation model to reveal facial pore changes after using skincare products. Our simulation pipeline consists of 2 steps: training data establishment and facial pore simulation. To establish training data, we collect face images with various pore quality indexes from short-term (8-weeks) clinical studies. People often experience significant skin fluctuations (due to natural rhythms, external stressors, etc.,), which introduces large perturbations in clinical data. To address this problem, we propose a sliding window mechanism to clean data and select representative index(es) to represent facial pore changes. Facial pore simulation stage consists of 3 modules: UNet-based segmentation module to localize facial pores; regression module to predict time-dependent warping hyperparameters; and deformation module, taking warping hyperparameters and pore segmentation labels as inputs, to precisely deform pores accordingly. The proposed simulation is able to render realistic facial pore changes. And this work will pave the way for future research in facial skin simulation and skincare product developments.

No doubt, we've entered the golden age of skincare. With customers spending billions of dollars a year on creams, serums, and cleansers that promise such miracles as wrinkle reduction, faded dark spots, improved elasticity, and beyond--you would think we'd be getting our fix. But even with a surfeit of products on the market, consumers are finding themselves increasingly frustrated with their unsolved skin struggles--and brands' broken promises. Founded by Harvard MBA Ming Zhao as well as Stanford PhD and computational physicist Amy Yuan, the up-and-coming beauty brand harnesses the power of science to transform the way we approach our skincare. With the understanding that no two people's skin is exactly the same, they set out to develop a data-driven model that delivers customized products made just for you, taking into account not only your skin type and skin concerns, but also your environment and your lifestyle.

Not all skin (or skincare) is created equal. One of the biggest challenges facing skincare consumers today is finding products that work for them--their unique skin type and the unique challenges they face with their skin. And, you won't find it on Google. The one-size-fits-all approach that most skincare and beauty companies take just doesn't deliver the unique skincare solutions consumers need to get their healthiest complexion. This doesn't give individuals the personalization they need.

Not all skin (or skincare) is created equal. One of the biggest challenges facing skincare consumers today is finding products that work for them--their unique skin type and the unique challenges they face with their skin. And, you won't find it on Google. The one-size-fits-all approach that most skincare and beauty companies take just doesn't deliver the unique skincare solutions consumers need to get their healthiest complexion. This doesn't give individuals the personalization they need.

Not all skin (or skincare) is created equal. One of the biggest challenges facing skincare consumers today is finding products that work for them--their unique skin type and the unique challenges they face with their skin. And, you won't find it on Google. The one-size-fits-all approach that most skincare and beauty companies take just doesn't deliver the unique skincare solutions consumers need to get their healthiest complexion. This doesn't give individuals the personalization they need.

Not all skin (or skincare) is created equal. One of the biggest challenges facing skincare consumers today is finding products that work for them--their unique skin type and the unique challenges they face with their skin. And, you won't find it on Google. The one-size-fits-all approach that most skincare and beauty companies take just doesn't deliver the unique skincare solutions consumers need to get their healthiest complexion. This doesn't give individuals the personalization they need.

The cosmetics market remains as lucrative as ever, if the latest estimates are anything to go by. It's anticipated to be worth $806 billion by 2023, driven in part by spending on AI in retail, which alone is expected to top $7.3 billion by 2022 thanks to blossoming tech like computer vision. L'Oréal has its finger on the pulse. Following on the heels of My Skin Track pH, a strip co-developed with skincare brand La Roche-Posay that can measure skin acid on the fly, it today debuted the Perso, an AI-powered system that's designed to deliver personalized skincare and cosmetic formulas. The Perso, which measures 6.5 inches tall and weighs just over a pound, features an automatic mechanism that dispenses portioned doses of product at its top.

It might sound unlikely that the answer to all your skincare questions is a selfie, but artificial intelligence is increasingly making that a possibility for women looking to improve their skin. Skin analysis applications that use AI-technology, like Olay's Skin Advisor, can provide you with a skin assessment in the click of a (camera) button, by analysing a picture of your face, alongside a short questionnaire about your skin concerns. This analysis can help take some of the guess work out of your skincare choices, by highlighting the areas of your face you may want to focus on (for example, smile lines or forehead wrinkles) and the perceived age and quality of your skin. Most applications also recommend targeted skincare products that might be suitable for you, cutting out some of the trial and error of searching a crowded beauty hall. Olay have dubbed their web-based platform a "personal at-home beauty advisor" and the process is certainly accessible, with most of the online and app-based programs that use AI-technology available completely free and on-the-go.