Why is okra so slimy? The plant's signature goo helps it thrive in the heat. Okra gets its slime from a substance called mucilage. Breakthroughs, discoveries, and DIY tips sent six days a week. Okra is one of those vegetables with a polarizing reputation.

Neurological disorders that affect speech production, such as Alzheimer's Disease (AD), significantly impact the lives of both patients and caregivers, whether through social, psycho-emotional effects or other aspects not yet fully understood. Recent advancements in Large Language Model (LLM) architectures have developed many tools to identify representative features of neurological disorders through spontaneous speech. However, LLMs typically lack interpretability, meaning they do not provide clear and specific reasons for their decisions. Therefore, there is a need for methods capable of identifying the representative features of neurological disorders in speech and explaining clearly why these features are relevant. This paper presents an explainable LLM method, named SLIME (Statistical and Linguistic Insights for Model Explanation), capable of identifying lexical components representative of AD and indicating which components are most important for the LLM's decision. In developing this method, we used an English-language dataset consisting of transcriptions from the Cookie Theft picture description task. The LLM Bidirectional Encoder Representations from Transformers (BERT) classified the textual descriptions as either AD or control groups. To identify representative lexical features and determine which are most relevant to the model's decision, we used a pipeline involving Integrated Gradients (IG), Linguistic Inquiry and Word Count (LIWC), and statistical analysis. Our method demonstrates that BERT leverages lexical components that reflect a reduction in social references in AD and identifies which further improve the LLM's accuracy. Thus, we provide an explainability tool that enhances confidence in applying LLMs to neurological clinical contexts, particularly in the study of neurodegeneration.

Significant strides have been made using large vision-language models, like Stable Diffusion (SD), for a variety of downstream tasks, including image editing, image correspondence, and 3D shape generation. Inspired by these advancements, we explore leveraging these extensive vision-language models for segmenting images at any desired granularity using as few as one annotated sample by proposing SLiMe. SLiMe frames this problem as an optimization task. Specifically, given a single training image and its segmentation mask, we first extract attention maps, including our novel "weighted accumulated self-attention map" from the SD prior. Then, using the extracted attention maps, the text embeddings of Stable Diffusion are optimized such that, each of them, learn about a single segmented region from the training image. These learned embeddings then highlight the segmented region in the attention maps, which in turn can then be used to derive the segmentation map. This enables SLiMe to segment any real-world image during inference with the granularity of the segmented region in the training image, using just one example. Moreover, leveraging additional training data when available, i.e. few-shot, improves the performance of SLiMe. We carried out a knowledge-rich set of experiments examining various design factors and showed that SLiMe outperforms other existing one-shot and few-shot segmentation methods.

A team of researchers affiliated with a host of entities in China has created a type of magnetic slime that can be configured on the fly to perform a variety of robotic tasks. In their paper published in the journal Advanced Functional Materials, the group describes their slime, possible uses for it and the actions they have taken to make it less toxic. Over the past several years, scientists have developed a variety of soft robots meant for possible use in the human body as therapeutic devices. In this new effort, the researchers have added to that list a type of slime that might one day be used to retrieve material swallowed accidentally or to repair internal injuries. Most soft robots meant for use in the body are extremely small, allowing them to move within arteries or veins and into organs.

Soft robots may soon be more flexible than ever... and a tad creepy. As The Guardian reports, researchers have developed a magnetic slime "robot" that can shift into different shapes to grab objects. It can encircle a group of pellets, for instance, and even stretch out in multiple directions to grab items on opposite sides. The result might induce some nightmares for the squeamish and is more than a little reminiscent of Spider-Man's symbiotic Venom, but it's surprisingly effective. The slime is made from the blend of polyvinyl alcohol (a polymer), borax and neodymium magnet particles.

Looking for video games that don't involve shooting enemies or require too much brain power? If so, you're in luck because there are many relaxing options to choose from, most of which can be played at your own pace and with family members. From creating your dream house in The Sims 4 to exploring the breathtaking depths of the ocean in ABZU, here are ten ultra-chill video games to play. For those who grew up playing The Sims in the noughties, now might be the perfect time to once again wile away the hours creating and controlling characters, building and decorating houses and exploring vibrant worlds. And, if in need of some extra simoleons, don't forget to use the money cheat'motherlode'.

Investment managers are increasingly looking to use technology to generate new trading ideas. Hedge funds in the U.S. and Europe now spend more than $170 million annually on so-called alternative data, according to a survey by Greenwich Associates. Though small, this deal announced Friday is the latest in a string of consolidation among new financial data providers. Advanced Publications Inc. acquired 1010data, an alternative data provider, for $500 million in 2015. Kensho Technologies, which applies artificial intelligence to stock research, announced this year that it would be bought by S&P Global Inc. for $550 million.