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Category: information science – Page 27

Creating realistic 3D models for applications like virtual reality, filmmaking, and engineering design can be a cumbersome process requiring lots of manual trial and error.

While generative artificial intelligence models for images can streamline artistic processes by enabling creators to produce lifelike 2D images from text prompts, these models are not designed to generate 3D shapes. To bridge the gap, a recently developed technique called Score Distillation leverages 2D image generation models to create 3D shapes, but its output often ends up blurry or cartoonish.

MIT researchers explored the relationships and differences between the algorithms used to generate 2D images and 3D shapes, identifying the root cause of lower-quality 3D models. From there, they crafted a simple fix to Score Distillation, which enables the generation of sharp, high-quality 3D shapes that are closer in quality to the best model-generated 2D images.

A new AI method enables the generation of sharp, high-quality 3D shapes that are closer to the quality of the best 2D image models. Previous approaches typically generated blurry or cartoonish 3D shapes.

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All DNA is prone to fragmentation, whether it is derived from a biological matrix or created during gene synthesis; thus, any DNA sample will contain a range of fragment sizes. To really exploit the true benefits of long read sequencing, it is necessary to remove these shorter fragments, which might other wise be sequenced preferentially.

DNA size selection can exclude short fragments, maximizing data yields by ensuring that those fragments with the most informational content are not blocked from accessing detection centers (for example, ZMWs) by shorter DNA fragments.

Next-generation size-selection solutions Starting with clean, appropriate-length fragments for HiFi reads can accelerate research by reducing the computation and data processing time needed post-sequencing. Ranger Technology from Yourgene Health is a patent-protected process for automating electrophoresis-based DNA analysis and size selection. Its fluorescence machine vision system and image analysis algorithms provide real-time interpretation of the DNA separation process.

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Avalo, a crop development company based in North Carolina, is using machine learning models to accelerate the creation of new and resilient crop varieties.

The traditional way to select for favorable traits in crops is to identify individual plants that exhibit the trait – such as drought resistance – and use those plants to pollinate others, before planting those seeds in fields to see how they perform. But that process requires growing a plant through its entire life cycle to see the result, which can take many years.

Avalo uses an algorithm to identify the genetic basis of complex traits like drought, or pest resistance in hundreds of crop varieties. Plants are cross-pollinated in the conventional way, but the algorithm can predict the performance of a seed without needing to grow it – speeding up the process by as much as 70%, according to Avalo chief technology officer Mariano Alvarez.

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In recent years, engineers have been trying to create hardware systems that better support the high computational demands of machine learning algorithms. These include systems that can perform multiple functions, acting as sensors, memories and computer processors all at once.

Researchers at Peking University recently developed a new reconfigurable neuromorphic computing platform that integrates sensing and computing functions in a single device. This system, outlined in a paper published in Nature Electronics, is comprised of an array of multiple phototransistors with one memristor (MP1R).

“The inspiration for this research stemmed from the limitations of traditional vision computing systems based on the CMOS von Neumann architecture,” Yuchao Yang, senior author of the paper, told Tech Xplore.

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In 2018, Google DeepMind’s AlphaZero program taught itself the games of chess, shogi, and Go using machine learning and a special algorithm to determine the best moves to win a game within a defined grid. Now, a team of Caltech researchers has developed an analogous algorithm for autonomous robots—a planning and decision-making control system that helps freely moving robots determine the best movements to make as they navigate the real world.

“Our algorithm actually strategizes and then explores all the possible and important motions and chooses the best one through dynamic simulation, like playing many simulated games involving moving robots,” says Soon-Jo Chung, Caltech’s Bren Professor of Control and Dynamical Systems and a senior research scientist at JPL, which Caltech manages for NASA. “The breakthrough innovation here is that we have derived a very efficient way of finding that optimal safe motion that typical optimization-based methods would never find.”

The team describes the technique, which they call Spectral Expansion Tree Search (SETS), in the December cover article of the journal Science Robotics.

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AWS and NVIDIA are teaming up to address one of the biggest challenges in quantum computing: integrating classical computing into the quantum stack, according to an AWS Quantum Technologies blog post. This partnership brings NVIDIA’s open-source CUDA-Q quantum development platform to Amazon Braket, enabling researchers to design, simulate and execute hybrid quantum-classical algorithms more efficiently.

Hybrid computing — where classical and quantum systems work together — is actually a facet of all quantum computing applications. Classical computers handle tasks like algorithm testing and error correction, while quantum computers tackle problems beyond classical reach. As quantum processors improve, the demand for classical computing power grows exponentially, especially for tasks like error mitigation and pre-processing.

The collaboration between AWS and NVIDIA is designed to ease this transition by providing researchers with seamless access to NVIDIA’s CUDA-Q platform directly within Amazon Braket. This integration allows users to test their programs using powerful GPUs, then execute the same programs on quantum hardware without extensive modifications.

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Despite technological advances like electronic health records (EHRs) and dictation tools, the administrative load on healthcare providers has only grown, often overshadowing the time and energy dedicated to direct patient care. This escalation in clerical tasks is a major contributor to physician burnout and dissatisfaction, affecting not only the well-being of providers but also the quality of care they deliver.

During consultations, the focus on documentation can detract from meaningful patient interactions, resulting in fragmented, rushed, and sometimes impersonal communication. The need for a solution that both streamlines documentation and restores the patient-centred nature of healthcare has never been more pressing. This is where AI-powered medical scribes come into play, offering a promising path from traditional dictation to fully automated, integrated documentation support.

AI medical scribe software utilises advanced artificial intelligence and machine learning to transcribe, in real time, entire patient-physician consultations without the need for traditional audio recordings. Leveraging sophisticated speech recognition and natural-language processing (NLP) algorithms, AI scribes are capable of interpreting and processing complex medical conversations with impressive accuracy. These systems can intelligently filter out non-essential dialogue, such as greetings and small talk, to create a streamlined and detailed clinical note.

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A new study from Washington University School of Medicine in St. Louis describes an innovative method of analyzing mammograms that significantly improves the accuracy of predicting the risk of breast cancer development over the following five years.

Using up to three years of previous mammograms, the new method identified individuals at high risk of developing 2.3 times more accurately than the standard method, which is based on questionnaires assessing clinical risk factors alone, such as age, race and family history of breast cancer.

The study is published Dec. 5 in JCO Clinical Cancer Informatics.

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New research from the Human Cell Atlas offers insights into cell development, disease mechanisms, and genetic influences, enhancing our understanding of human biology and health.

The Human Cell Atlas (HCA) consortium has made significant progress in its mission to better understand the cells of the human body in health and disease, with a recent publication of a Collection of more than 40 peer-reviewed papers in Nature and other Nature Portfolio journals.

The Collection showcases a range of large-scale datasets, artificial intelligence algorithms, and biomedical discoveries from the HCA that are enhancing our understanding of the human body. The studies reveal insights into how the placenta and skeleton form, changes during brain maturation, new gut and vascular cell states, lung responses to COVID-19, and the effects of genetic variation on disease, among others.

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