Branched optimal transport (BOT) is a generalization of optimal transport in which transportation costs along an edge are subadditive. This subadditivity models an increase in transport efficiency when shipping mass along the same route, favoring branched transportation networks. We here study the NP-hard optimization of BOT networks connecting a finite number of sources and sinks in \mathbb{R} 2 . First, we show how to efficiently find the best geometry of a BOT network for many sources and sinks, given a topology. Second, we argue that a topology with more than three edges meeting at a branching point is never optimal.

We introduce an offline multi-agent reinforcement learning ( offline MARL) framework that utilizes previously collected data without additional online data collection. Our method reformulates offline MARL as a sequence modeling problem and thus builds on top of the simplicity and scalability of the Transformer architecture. In the fashion of centralized training and decentralized execution, we propose to first train a teacher policy as if the MARL dataset is generated by a single agent. After the teacher policy has identified and recombined the "good" behavior in the dataset, we create separate student policies and distill not only the teacher policy's features but also its structural relations among different agents' features to student policies. Despite its simplicity, the proposed method outperforms state-of-the-art model-free offline MARL baselines while being more robust to demonstration's quality on several environments.

Spiking Neural Network (SNN), recognized as a type of biologically plausible architecture, has recently drawn much research attention. This bio-mimetic mechanism of SNN demonstrates extreme energy efficiency since it avoids any multiplications on neuromorphic hardware. However, the forward-passing 0/1 spike quantization will cause information loss and accuracy degradation. To deal with this problem, the Information maximization loss (IM-Loss) that aims at maximizing the information flow in the SNN is proposed in the paper. The IM-Loss not only enhances the information expressiveness of an SNN directly but also plays a part of the role of normalization without introducing any additional operations (\textit{e.g.}, bias and scaling) in the inference phase.

WHEN PROFESSOR Yan Liu was growing up in Changchun, China, her father wanted her to become a doctor like him. When she chose computer science, "I was a tiny bit disappointed," Xiwen Liu, a retired anesthesiologist, acknowledges. But their worlds collided in 2011, when the elder Liu was diagnosed with prostate cancer. At 67, he was relatively young, had a healthy lifestyle and had no symptoms. Like many of the 1.2 million men diagnosed with prostate cancer each year, he had surgery to remove tumors in his prostate, but he suffered from incontinence, bleeding and infections for years after the surgery.

Microsoft's 2023 Future of Finance Trends report found that financial teams and leaders are struggling to strike the right balance between strategic innovation and protecting the financial health of their organisations, with 88 per cent feeling overwhelmed by the amount of data they are handling. The report concludes that AI and automation are vital for growth, with 82 per cent of finance leaders believing that technology is necessary to support business goals by freeing up valuable time for finance teams. However, 89 per cent of organisations do not have integrated automation, analytics or AI, according to KPMG's 2021 The Future of Finance report, meaning that they are losing out on valuable benefits in terms of time and money. "The key to advancing business and staying on top of the problems we're currently facing is to be data-driven by building predictive and analytical capabilities that will deliver the right insights to make well-informed decisions," says Roni Karassik, director of product for Microsoft Cloud for Financial Services and industry AI. "AI applications provide three overarching benefits –transforming the customer experience, empowering employees and providing deeper insights." Firms are looking to use AI to do more with less by automating and processing data more effectively.

Barbie fans around the world are counting down to the release of the Barbie movie, which is set to land in theatres on July 21, 2023. Posters for the film dropped yesterday, showing stars including Margot Robbie, 32, and Ryan Gosling, 42 in their iconic roles. But have you ever wondered what your character might look like in Barbieland? Well now you can find out, thanks to a new tool dubbed the Barbie Selfie Generator. The tool uses artificial intelligence (AI) to transform your photos into Barbie movie posters - here's how to try it yourself.

There is a brand new camera that's hitting the market today; and with the right marketing, it could upset the market that Google and Apple have been trying to disrupt for a long time now. Midjourney AI has today announced a brand new camera that can be brought with you anywhere to create AI visuals and apply them to whatever you see in front of you. The company claims that it brings the power of their AI to a retro-inspired camera body so that creators can make AI Visuals to their heart's content. Effectively, they're eliminating post-production processes that Adobe brings photographers by putting it all right into the single device. This is a sponsored blog post presented by Midjourney.

In this series of articles, I will go through protein folding and deep learning models such as AlphaFold, OmegaFold, and ESMFold. We will start with AlphaFold2! Proteins are molecules that perform most of the biochemical functions in living organisms. They are involved in digestion (enzymes), structural processes (keratin -- skin), photosynthesis and are also used extensively in the pharmaceutical industry [2]. The 3D structure of the protein is fundamental to its function.

In this PyTorch tutorial we will learn how to create Logistic Regression Classifier in PyTorch. Logistic regression is a type of regression that predicts the probability of an event. It is used for classification problems and has many applications in the fields of machine learning, artificial intelligence, and data mining. The formula of logistic regression is to apply a sigmoid function to the output of a linear function. This article discusses how you can build a logistic regression classifier.

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