Lifeboat Foundation: Safeguarding Humanity
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Blog – Page 4700

Skin-like electronics could seamlessly integrate with the body for applications in health monitoring, medication therapy, implantable medical devices, and biological studies.

With the help of the Polsky Center for Entrepreneurship and Innovation, Sihong Wang, an assistant professor of molecular engineering at the University of Chicago’s Pritzker School of Molecular Engineering, has secured patents for the building blocks of these novel devices.

Drawing on innovation in the fields of semiconductor physics, solid mechanics, and energy sciences, this work includes the creation of stretchable polymer semiconductors and transistor arrays, which provide exceptional electrical performance, high semiconducting properties, and mechanical stretchability. Additionally, Wang has developed triboelectric nanogenerators as a new technology for harvesting energy from a user’s motion—and designed the associated energy storage process.

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At this year’s Conference on Machine Learning and Systems (MLSys), we and our colleagues presented a new auto-scheduler called DietCode, which handles dynamic-shape workloads much more efficiently than its predecessors. Where existing auto-encoders have to optimize each possible shape individually, DietCode constructs a shape-generic search space that enables it to optimize all possible shapes simultaneously.

We tested our approach on a natural-language-processing (NLP) task that could take inputs ranging in size from 1 to 128 tokens. When we use a random sampling of input sizes that reflects a plausible real-world distribution, we speed up the optimization process almost sixfold relative to the best prior auto-scheduler. That speedup increases to more than 94-fold when we consider all possible shapes.

Despite being much faster, DietCode also improves the performance of the resulting code, by up to 70% relative to prior auto-schedulers and up to 19% relative to hand-optimized code in existing tensor operation libraries. It thus promises to speed up our customers’ dynamic-shaped machine learning workloads.

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Due to their self-assembly function, DNA sensors have gained much attention as next-generation sensors that require an extremely low power supply.

Study: Spin transport properties in DNA & electrically doped iron QD organo-metallic junction. Image Credit: marie_mi/Shutterstock.com.

Scientists have recently used iron (Fe) quantum dots (QD) electrodes to determine the spin transport properties and quantum scattering transmission characteristics of DNA sensors at room temperature. This study is available in Materials Today: Proceedings.

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Explaining the potential of nanotubes further, one of the lead researchers and associate professor at Johns Hopkins University (JHU), Rebecca Schulman told IE, “Tinier plumbing might help us analyze individual molecules, which could help us make better drugs or enzymes, separate toxins, or even create better batteries by designing the conduits that ions flow through rather than using a porous material.”

She believes that although these technologies are still 10+ years away, their foundation is in things like nano-plumbing and being able to precisely measure and control the pipes the plumbing is made of.

Nanotubes are a highly evolved version of nanopores, small DNA structures proposed in some previously published studies. A nanopore is designed to serve as a conduit across a thin barrier between two chambers. Examples of such barriers are cell membranes (nanopores allow things to move in and out of a cell) and across metal or graphene sheets (like in nanopore-enabled DNA sequencing).

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AI image generation is here in a big way. A newly released open source image synthesis model called Stable Diffusion allows anyone with a PC and a decent GPU to conjure up almost any visual reality they can imagine. It can imitate virtually any visual style, and if you feed it a descriptive phrase, the results appear on your screen like magic.

Some artists are delighted by the prospect, others aren’t happy about it, and society at large still seems largely unaware of the rapidly evolving tech revolution taking place through communities on Twitter, Discord, and Github. Image synthesis arguably brings implications as big as the invention of the camera—or perhaps the creation of visual art itself. Even our sense of history might be at stake, depending on how things shake out. Either way, Stable Diffusion is leading a new wave of deep learning creative tools that are poised to revolutionize the creation of visual media.

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A new study has identified an association between receiving an influenza vaccine and a reduced risk of stroke. The research is published in the journal Neurology.

Risk factors for stroke

A stroke occurs when the blood supply to the brain is cut off, causing damage to neuronal cells that in turn affects physiological functions in the body. There are different types of strokes that can occur: ischemic – where a blockage prevents blood from reaching the brain, hemorrhagic – caused by a bleed in or around the brain and transient ischemic attacks (TIA) which are strokes that last for a short amount of time. It’s estimated that one in four people aged 25 and over will be afflicted by a stroke in their lifetime.

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Stable Diffusion is a powerful open-source image AI that competes with OpenAI’s DALL-E 2. The AI training was probably rather cheap in comparison.

Anyone interested can download the model of the open-source image AI Stable Diffusion for free from Github and run it locally on a compatible graphics card. This must be reasonably powerful (at least 5.1 GB VRAM), but you don’t need a high-end computer.

In addition to the local, free version, the Stable Diffusion team also offers access via a web interface. For about $12, you get roughly 1,000 image prompts.

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