Articles tagged with Algorithms
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.
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 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...
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.
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 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.
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.
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 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.
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.
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.
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.
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 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 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.
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.
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.
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 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 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.
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 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 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 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.
A new study by New York University found that YouTube's recommendation algorithm prioritized election-fraud-related videos for users already skeptical about the 2020 presidential election's legitimacy. This highlights the dangers of opaque algorithms perpetuating misinformation and disinformation.
Researchers at Chalmers University of Technology developed a computer model to predict enzyme efficiency. This helps find efficient cell factories for producing biotech products like biofuels and medicines, and studies difficult diseases.
Xiu Yang, a 2022 NSF CAREER award recipient, is working on an algorithmic approach to model and overcome hardware errors in quantum computing. He aims to enable the technology to achieve its promise of unparalleled speed in solving complex problems.
A Brazilian research team has developed a novel method to sort specialty and standard coffee beans using multispectral imaging and machine learning. The technique, which does not require roasting or human intervention, uses images of the beans at different wavelengths to distinguish between quality levels.
A team of Japanese researchers used reinforcement learning to study fluid mixing during laminar flow, achieving exponentially fast mixing without prior knowledge. The method also enabled effective transfer learning, reducing training time for new mixing problems, and has potential applications across various industries.
Researchers designed a centered error entropy-based sigma-point Kalman Filter to enhance the filtering algorithm's robustness in spacecraft attitude determination. The proposed CEEUKF outperformed classical methods and other robust algorithms in simulating non-Gaussian noise, achieving higher accuracy and faster convergence rates.
Rice University's ROBE Array algorithm slashes the size of DLRM memory structures, allowing training on 100 megabytes of memory and a single GPU. The method matches state-of-the-art DLRM training methods with improved inference efficiency.
Researchers at the University of Oldenburg and Fraunhofer IWES collaborate on a new project to develop more accurate wind flow simulations using artificial intelligence. The goal is to reduce computing times and enhance precision, ultimately accelerating innovation in wind turbine design.
Researchers at MIT developed an AI model that can detect Parkinson's disease from breathing patterns, using a neural network to assess the presence and severity of the condition. The device is non-invasive and can be used in patients' homes without any bodily contact.
Researchers at NC State University have developed a cooperative distributed algorithm that allows autonomous vehicle software to make calculations more quickly, enabling real-time navigation of complex merging scenarios. The approach improves both traffic flow and safety, with zero incidents in simulations.
Scientists at Kyoto University developed two methods to identify RNA modifications impacting gene regulation and disease. Their approach uses probability algorithms with high-throughput sequencing technology, distinguishing pseudouridine substitutions from other base changes.
Bhattacharya's project uses topological abstraction to reduce complexity in robotic systems, enabling more efficient and accurate motion planning. The approach has potential applications in industries such as transportation, manufacturing, and healthcare.
Researchers used natural language processing and machine learning to analyze nearly 35,500 death records, identifying the most common substances involved in overdose deaths. The system reduced data processing time by months, allowing for more rapid public health responses and interventions.
Researchers at Princeton University used artificial intelligence to simulate ice formation by individual atoms and molecules with quantum accuracy. This breakthrough enables tracking of hundreds of thousands of atoms over longer timespans than previous simulations.
Researchers used machine learning algorithms and k-D tree data structure to identify 11 previously undetected space anomalies, seven of which are supernova candidates. The team analyzed digital images of the Northern sky taken in 2018 using a k-D tree to detect anomalies through the 'nearest neighbour' method.
Researchers optimized the ZZ SWAP network protocol, introducing a new technique to improve quantum error mitigation. This enables more efficient execution of quantum algorithms like QAOA, which can solve combinatorial optimization problems.
Researchers argue that using tools to estimate racial and ethnic information can identify algorithmic bias and combat disparities in healthcare. This approach can lead to more equitable pay-for-performance schemes and better clinical decision-making.