Machine learning (ML) enables the development of powerful methods for predicting thermophysical properties with unprecedented scope and accuracy. However, technical barriers like cumbersome implementation in established workflows hinder their application in practice. With MLPROP, we provide an interactive web interface for directly applying advanced ML methods to predict thermophysical properties without requiring ML expertise, thereby substantially increasing the accessibility of novel models. MLPROP currently includes models for predicting the vapor pressure of pure components (GRAPPA), activity coefficients and vapor-liquid equilibria in binary mixtures (UNIFAC 2.0, mod. UNIFAC 2.0, and HANNA), and a routine to fit NRTL parameters to the model predictions. MLPROP will be continuously updated and extended and is accessible free of charge via https://ml-prop.mv.rptu.de/. MLPROP removes the barrier to learning and experimenting with new ML-based methods for predicting thermophysical properties. The source code of all models is available as open source, which allows integration into existing workflows.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. In our eighth grade classroom, her name was Hanna. On AOL Instant Messenger, she was Banana3017. I was in love with both. At school, she was funny, and kind, and she had blue eyes that made my cheeks glow the same fiery color as her hair when she looked at me.

WEHEAD connects to ChatGPT and displays a face, expressions and voice. China is reportedly working to cognitively merge humans with machines as part of its ongoing efforts to compete in the artificial intelligence race. The communist country is using brain-computer interface (BCI) technology -- systems that allow for communication between the brain and an external device -- to "augment human cognition and human-machine teaming," The Washington Times reported, citing a presentation from Georgetown experts delivered to U.S. officials. These include invasive, minimally-invasive and non-invasive BCIs, according to The Washington Times. Invasive BCIs involve surgery to implant electrodes into the brain, while non-invasive BCIs use sensors on the scalp to monitor brain activity. Meanwhile, minimally-invasive BCIs involve implanting devices, but they do not penetrate brain tissue, according to a report in the National Library of Medicine.

Does Feedback Help in Bandits with Arm Erasures? Abstract --We study a distributed multi-armed bandit (MAB) problem over arm erasure channels, motivated by the increasing adoption of MAB algorithms over communication-constrained networks. In this setup, the learner communicates the chosen arm to play to an agent over an erasure channel with probability ϵ [0, 1); if an erasure occurs, the agent continues pulling the last successfully received arm; the learner always observes the reward of the arm pulled. In past work, we considered the case where the agent cannot convey feedback to the learner, and thus the learner does not know whether the arm played is the requested or the last successfully received one. In this paper, we instead consider the case where the agent can send feedback to the learner on whether the arm request was received, and thus the learner exactly knows which arm was played. Surprisingly, we prove that erasure feedback does not improve the worst-case regret upper bound order over the previously studied no-feedback setting. In particular, we prove a regret lower bound of Ω( KT + K/ (1 ϵ)), where K is the number of arms and T the time horizon, that matches no-feedback upper bounds up to logarithmic factors. We note however that the availability of feedback does enable to design simpler algorithms that may achieve better constants (albeit not better order) regret bounds; we design one such algorithm, and numerically evaluate its performance. The multi-armed bandit (MAB) framework has emerged as a fundamental model for sequential decision-making under uncertainty, finding applications in areas such as recommendation systems, clinical trials, distributed robotics, and online advertising [1].

On a recent Wednesday night in Los Angeles, I was ready to buy a hot dog with my face. I was at the Intuit Dome, a 2 billion entertainment complex that opened earlier this month. Soon, it will be the home of the L.A. Clippers, but I was there to watch Olivia Rodrigo, queen of teen angst, perform a sold-out show. The arena was filled with people wearing purple cowboy hats and the same silver sequin miniskirt, all of us ready to scream-sing for two hours straight. But first, we needed food.

We present the first hard-constraint neural network for predicting activity coefficients (HANNA), a thermodynamic mixture property that is the basis for many applications in science and engineering. Unlike traditional neural networks, which ignore physical laws and result in inconsistent predictions, our model is designed to strictly adhere to all thermodynamic consistency criteria. By leveraging deep-set neural networks, HANNA maintains symmetry under the permutation of the components. Furthermore, by hard-coding physical constraints in the network architecture, we ensure consistency with the Gibbs-Duhem equation and in modeling the pure components. The model was trained and evaluated on 317,421 data points for activity coefficients in binary mixtures from the Dortmund Data Bank, achieving significantly higher prediction accuracies than the current state-of-the-art model UNIFAC. Moreover, HANNA only requires the SMILES of the components as input, making it applicable to any binary mixture of interest. HANNA is fully open-source and available for free use.

While Barbenheimer was undoubtedly the biggest movie story of 2023, the year in film was one jam-packed with dozens of truly great movies--not all of which managed to generate the nonstop headlines or mainstream traction that an iconic doll and the "father of the atomic bomb" did. It was a stellar year for first-time directors as well, as evidenced by films like Emily, The Unknown Country, and A Thousand and One. If you've seen Barbie, Oppenheimer, and many of the year's higher-profile movies, here are 15 that you maybe haven't seen that are definitely worth your time. If you buy something using links in our stories, we may earn a commission. This helps support our journalism.

ChatGPT can now browse the internet to provide users with current information, its parent company OpenAI has announced. The chatbot was previously trained to use data up to September 2021 and was unable to provide real-time information. On Wednesday, Microsoft-backed OpenAI announced on X, formerly Twitter, that the new update allows it to move past the September 2021 cutoff and access current information on the internet. ChatGPT can now browse the internet to provide you with current and authoritative information, complete with direct links to sources. It is no longer limited to data before September 2021.

The last time the Writers Guild of America went on strike, in 2007, workers pushed back against the nascent streaming industry, advocating for higher residual payments for content released over streaming. Now a new technology, artificial intelligence, stands to drastically change Hollywood again as Guild strikers return to the picket line. Streaming giants like Hulu, Netflix, and Disney have come to dominate the industry, changing the models by which content is produced and distributed and making it increasingly difficult for writers to earn a sustainable income. And as artificial intelligence technology rapidly improves, the WGA aims to place limits on the use of AI in movies and TV scripts. The Alliance of Motion Picture and Television Producers (AMPTP) --which is negotiating the contract on behalf of Hollywood studios, streamers, and production companies--say their priority is "the long-term health and stability of the industry" and they are dedicated to reaching "a fair and reasonable agreement" according to the Associated Press.

From healthcare and education to finance and policing, artificial intelligence (AI) is becoming increasingly embedded in people's daily lives. Despite being posited by advocates as a dispassionate and fairer means of making decisions, free from the influence of human prejudice, the rapid development and deployment of AI has prompted concern over how the technology can be used and abused. These concerns include how it affects people's employment opportunities, its potential to enable mass surveillance, and its role in facilitating access to basic goods and services, among others. In response, the organisations that design, develop and deploy AI technologies – often with limited input from those most affected by its operation – have attempted to quell people's fears by setting out how they are approaching AI in a fair and ethical manner. Since around 2018, this has led to a deluge of ethical AI principles, guidelines, frameworks and declarations being published by both private organisations and government agencies around the world.