A team of researchers from Xi'an Jiaotong-Liverpool University and other institutions has identified a flexible and user-friendly model for predicting flood frequency in a changing environment. The fractional polynomial-based regression method is more effective than existing models, which often fail to account for factors like climate ...
Researchers developed a new method using Bayesian estimation to reduce X-ray fluorescence analysis time from 7 seconds to 3 seconds per measurement point, saving up to 11 hours for large-scale elemental distributions.
Researchers used machine learning to analyze parole data from New York and found that the New York State Parole Board could safely grant parole to more inmates, potentially doubling the release rate. The study suggests eliminating racial disparities in release rates while maintaining public safety.
Researchers at Drexel University used GPT-3 to spot early signs of Alzheimer's in spontaneous speech, achieving 80% accuracy. The program analyzed word-use, sentence structure and meaning from transcripts to identify characteristic profiles of Alzheimer's speech.
A recent study has identified common and unique cellular processes in six neurodegenerative diseases, providing new insights into the underlying causes of these conditions. The research used machine learning analysis to compare RNA markers in whole blood samples from patients with distinct diseases, revealing eight shared themes across...
A new algorithm developed by researchers at Charité improves motor symptoms comparable to standard of care treatment, increasing efficiency in deep brain stimulation for Parkinson's disease. The study suggests a promising result for imaging-based algorithms to simplify clinical practice and improve therapeutic outcomes.
New signal-processing algorithms have been shown to help mitigate the impact of turbulence in free-space optical experiments. The researchers achieved record results using commercially available photonic lanterns and a spatial light modulator to emulate turbulence.
Researchers from Brown and MIT developed a new framework that uses machine learning and sequential sampling to predict rare disasters like earthquakes and pandemics with less data. The framework, called DeepOnet, has been shown to outperform traditional modeling efforts in predicting scenarios, probabilities and timelines of rare events.
A team of researchers from Tokyo University of Science developed a super-hierarchical and explanatory analysis method for magnetic reversal processes, enabling the detection of subtle microscopic changes. The new algorithm can predict stable/metastable states in advance and improve the reliability of spintronics devices.
A University of Houston researcher has developed a method to describe complex systems using the least number of variables possible, reducing complexity from millions to just one. This advancement speeds up science with efficiency and ability to understand and predict natural system behavior.
A new, non-invasive malaria detection tool developed by a University of Queensland-led team can quickly identify entire villages or towns suffering from the disease. The device uses infrared-light and is chemical-free, needle-free, and detects malaria through the skin.
Researchers at MIT have developed a scheme for private information retrieval that is about 30 times faster than other comparable methods. The technique enables users to search an online database without revealing their query to the server, with potential applications in private communication and targeted advertising.
Researchers develop an algorithm to automate feature extraction from HD maps and point cloud data. The model achieved high precision in detecting road signs and traffic lights, with zero false detections.
A new study reveals that the US Midwest has seen a significant increase in cover crop adoption, with 7.2% of cropland being planted with cover crops in 2021. This is attributed to government programs and funding initiatives, which have been shown to strongly correlate with the onset of cover crop assistance.
A team of researchers proposes an intelligent routing scheme to optimize link load balancing in software-defined networks, achieving significant performance improvements. Their algorithms outperform traditional methods, reducing maximum bandwidth by 24.6 percent in real-world topologies.
A team of researchers from the University of Pennsylvania has developed a new algorithm, metadynamics, that can navigate high-dimensional energy landscapes to find low-energy configurations. This breakthrough has the potential to revolutionize fields such as protein folding and machine learning.
Scientists used AI-driven PandaOmics platform to analyze gene expression datasets from DNA repair diseases, identifying biomarkers associated with treatment response. The study focused on genes that stratify cancer patients by survival outcomes, providing potential targets for personalized therapies.
A study led by Kyoto University researchers found that AI-generated haiku poems, created without human intervention, were often indistinguishable from those penned by humans. In contrast, human-AI collaboration produced more creative works.
Researchers at MIT have developed a new architecture for optical neural networks, which can perform complex linear algebra operations using light signals. The new design eliminates uncorrectable errors that limited the scalability of earlier systems, enabling larger networks with improved accuracy.
A new algorithm unravels the structure of financial services on the Ethereum blockchain, revealing highly intertwined structures that involve risks not yet fully understood. The findings highlight the need for transparency and awareness among users, regulators, and policymakers to mitigate systemic risks associated with cryptoassets.
Researchers found that algorithms classify white men better, reproduce false beliefs about physical attributes, and make stereotypical associations between men and sciences, women and arts. Under-representation of women in AI design and biased datasets contribute to the problem.
Researchers developed a new machine-learning framework that enables cooperative or competitive AI agents to consider the future behaviors of all agents, not just their teammates or competitors. This framework, FURTHER, uses two modules: an inference module and a reinforcement learning module, to enable agents to adapt their behaviors a...
Chugh argues that AI risk assessments raise concerns about human biases and the absence of individualized decision-making in the judiciary. She advocates for more research on AI's role in the court system, prioritizing community-driven and individualized processes.
Researchers have developed a new approach to phase retrieval in coherent X-ray imaging, using a complexity parameter to guide the algorithm. This methodology reduces artifacts and improves solution quality, resulting in higher-resolution images of micro- and nano-sized objects.
Researchers from the University of Johannesburg deployed Few Shot Learning (FSL) for NIALM, a non-intrusive appliance load monitoring system. FSL requires only 7 test images to recognize appliances with 97.83% accuracy, making it faster and more cost-effective than traditional Machine Learning.
A recent study by UCF Associate Professor Varadraj Gurupur created an algorithm to predict and measure the incompleteness of electronic health records. The analysis found that missing information is a significant issue, with varying levels of incompleteness per year and no clear pattern of where it occurs.
A new AI tool helps governments decide whether to bail out a bank by predicting if the intervention will save money for taxpayers. The algorithm assesses financial implications and suggests optimal bailout strategies.
Researchers created a Facebook-like prototype platform, called Trustnet, where users rate posts as accurate or inaccurate before sharing. This approach showed that people effectively assess misinforming posts and share their assessments with others.
A novel multi-modal image retrieval system, DenseBert4Ret, has been developed by researchers from Gwangju Institute of Science and Technology (GIST) using deep learning algorithms. The system outperforms state-of-the-art models in retrieving images based on both image and text features.
A team of Lehigh University researchers is investigating the role of online media in shaping facts and influencing behavior during the COVID-19 pandemic. The project aims to better understand how individuals and communities develop their understanding of major events, ultimately acting on that understanding.
A research team developed a smart mask integrating an ultrathin soundwave sensor that detects breathing, coughing, and speaking sounds. The mask uses machine-learning algorithms to identify respiratory diseases and improve public health by enabling prolonged monitoring.
A researcher developed a system to determine the pose of cooperative spacecraft using ToF cameras and reflector markers. The system consists of three steps: reflector detection, matching, and pose calculation, and was tested in experiments with high accuracy.
Deep learning models can become less accurate in recognizing specific categories of images, sounds, or text after network pruning. Researchers demonstrate a technique to address this challenge, improving the fairness of deep learning models.
Researchers at MIT have developed a machine-learning model that captures how sounds propagate through spaces, allowing for accurate visual renderings of rooms. This technique has potential applications in virtual and augmented reality, as well as improving AI agents' understanding of their environment.
Researchers at FAU aim to empower amputees to maximize their individual potential for controlling the full dexterity of artificial hands using a novel bimodal skin sensor and machine learning algorithms. The project will develop customized prosthetic sockets and training programs to overcome limitations with current sensing technology.
A novel algorithm uses near-infrared spectroscopy to estimate intracranial pressure (ICP) based on hemoglobin levels. The research validates the accuracy of this method using invasive ICP data.
A deep learning algorithm trained on adult populations shows improved sensitivity for diagnosing thyroid nodules in children, with higher accuracy than traditional ACR TI-RADS. The study suggests the potential benefits of this new approach for evaluating thyroid nodules in younger patients.
Researchers at Max Planck Institute successfully revived ancient enzymes, revealing a novel protein component that increased CO2 specificity in Rubisco. This discovery provides new insights into the evolution of modern photosynthesis and suggests adding new components may improve its efficiency.
A University of Groningen team created two machine learning models to predict app removal risks, achieving accuracy rates of up to 79.2%. The models can help developers avoid bans and users protect their data.
A team of Illinois Tech researchers used machine learning to estimate the age and gender of individual users with high accuracy, raising questions about data security and privacy. The study highlights the need for better regulations and best practices to protect personal information from being misused.
Researchers at the University of Pennsylvania have developed an algorithm that enables 2D materials to maintain their mechanical strength after conversion into 3D structures. The algorithm is inspired by kirigami art and mimics the structure of nacre, a natural shell coating known for its robust mechanical properties.
Researchers from McGill University and MIT developed an AI system that can learn the rules and patterns of human languages on its own. The model automatically generates higher-level language patterns that can be applied to different languages, achieving better results.
Researchers at North Carolina State University developed a blueprint for incorporating ethical guidelines into AI decision-making programs. The new mathematical formula, based on the Agent, Deed, and Consequence (ADC) Model, considers intent, character, and consequences of actions to make more informed decisions.
Researchers developed a smart mouthguard that translates complex bite patterns into instructions to control devices such as computers, smartphones and wheelchairs. The device achieves 98% accuracy and has the potential to support individuals with limited dexterity or neurological disorders.
New research highlights the dangers of AI-powered recruitment tools that claim to remove discrimination from hiring. The tools reduce race and gender to trivial data points and often rely on personality analysis that is
Researchers found that machine learning models outperformed traditional risk prediction models in predicting suicide-related outcomes. These models can identify patterns associated with suicide risk and have been shown to correctly predict 66% of people who would experience a suicide outcome.
A new study proposes a powerful computer-modeling approach to cell simulations, reaching unprecedented simulation timescales at all-atom resolution. The technique combines advantages of protein docking and molecular simulations, enabling faster and more precise treatment of human disease.
Researchers used machine learning algorithms to optimize climate models, increasing their accuracy and detail. By applying Generative Adversarial Networks (GANs) to climate simulations, the team was able to improve the models' ability to represent extreme precipitation events.
A new automated screening tool can accurately identify patients at high risk of developing progressive scarring of the lungs, a condition called idiopathic pulmonary fibrosis (IPF). The tool uses machine-learning algorithms to analyze patient electronic health records and detects IPF risk automatically.
Neuronal silencing periods enable efficient temporal sequence identification, allowing the brain to remember phone numbers and PINs. A new AI mechanism utilizing this mechanism also protects against stolen cards by recognizing personal handwriting style and timing.
A study published in Cell Press found that when humans are involved, computer decisions are perceived as fairer. Participants deemed decisions related to positive outcomes fairer than negative ones and had concerns over fairness in systems with higher stakes. The results suggest that automated decision-making systems need careful desig...
Researchers developed a universal screening tool for IPF that can alert primary care physicians to its possible presence, enabling earlier diagnosis and treatment. The Zero-burden Co-Morbidity Risk Score for IPF (ZCoR-IPF) algorithm uses existing patient records to identify patients at risk of developing the disease.
Researchers developed an algorithm to decode brain scans and identify epilepsy types based on electrical signal patterns. The Cumulative Sharp Count and areas under spike and sharp curves were used as parameters to detect epilepsy, with high accuracy rates in blind validation studies.
Researchers developed a computational platform to identify metabolic vulnerabilities in ovarian cancer genes, suggesting opportunities for targeted therapies. The study found that certain genetic alterations can create vulnerabilities in cancer cell metabolism, which can be exploited to selectively kill cancer cells.
Researchers developed an AI tool using natural language processing and machine learning to identify people who inject drugs in electronic health records. The model accurately identified PWIDs in 1,000 records from 2003-2014, significantly improving clinical decision making and resource allocation.
A study from the University of Georgia shows people who rely on algorithms for creative tasks don't improve their performance and are more likely to trust low-quality advice. Participants preferred algorithm-derived advice over human-based advice, even when confident in their answers.
Researchers have developed an algorithm that uses smartphone camera and flash to detect low blood oxygen levels. The method produced accurate results in 80% of the participants, showing promise for remote monitoring and early detection of conditions like COVID-19.
A large-scale experimental study by Harvard, Stanford, and MIT researchers found that weaker social connections on LinkedIn have a greater beneficial effect on job mobility than stronger ties. Weaker ties increased the likelihood of job mobility the most, while strongest ties had the least impact.
Healthcare researchers caution against misusing AI algorithms in clinical research, highlighting concerns about bias, transparency, and data quality. The team advocates for evaluating ML methods against traditional statistical approaches and ensuring clinician decision-making is complemented, not replaced.
A new study from MIT reveals that computer models predicting molecular interactions, like AlphaFold, need improvement to help identify drug mechanisms of action. Researchers improved the performance of these models using machine-learning techniques, but more work is needed.