Articles tagged with Algorithms
Researchers created synthetic hearts using statistical shape analysis to identify how heart shape relates to function. The study included 1000 new synthetic hearts made open access for testing and analysis.
A recent study by University of Georgia researchers found that people are more likely to rely on algorithms for decision-making, even when faced with complex challenges. As the task becomes harder, people prefer algorithm-generated suggestions over human advice or 'wisdom of the crowd', according to the study.
Researchers at Penn State develop a new framework for meta-assembly, allowing for accurate reconstruction of full-length mRNA sequences from short fragments. This enables better understanding of cell machinery and potential biomarkers for diagnosis.
Researchers at Shibaura Institute of Technology developed a camera-based support system that accurately detects tactile paving. The new image processing algorithm can detect tactile paving up to 91.65% of the time, improving safety for visually impaired individuals.
Researchers at UOC are developing a system to analyze and digitize agricultural data, using machine learning and intelligent algorithms to personalize customer targeting and improve sales processes. The project aims to give small enterprises more competitive tools, with potential impact on other sectors.
Christopher Musco, an assistant professor at NYU Tandon, received a $562,224 NSF grant to develop new algorithms for processing massive data sets. His project aims to democratize big-data modeling and advance US mathematics education through closer ties with STEM fields.
Researchers used ship movement data to estimate wave conditions in real time, without extra costs for measurement instruments. The approach combines cause-and-effect calculations with ship geometry and dynamic motions.
A new satellite-measured Solar-induced Chlorophyll Fluorescence (SIF) product is introduced to enhance carbon neutrality research. The SIF product, developed using a physical-based algorithm named IAPCAS/SIF, retrieves emission data from two micro-windows within the O2 A-band.
UofL and Medtronic will develop a closed-loop system to monitor and adjust stimulator settings for multiple functions, including locomotion and bladder control. The project aims to improve the lives of people with spinal cord injury by reducing manual interaction and increasing positive outcomes.
A machine learning algorithm has been shown to accurately predict adult patients at risk of attempting or experiencing suicidal thoughts. The Vanderbilt Suicide Attempt and Ideation Likelihood (VSAIL) model uses routine health records to identify those who should receive further screening, potentially directing limited resources where ...
After solving the sum of cubes puzzle for 42, researchers found a new solution for 3 using an optimized algorithm that utilized mathematical 'sieving' techniques. The discovery suggests there may be infinitely many more solutions for other values of k, with the next solution involving gigantic 21-digit numbers.
Clinical algorithms that adjust for race can cause harm to patients by delaying access to additional tests and treatments, according to a new article. The authors argue that the medical community must step up to achieve consensus on paths forward to address racial inequities in health care.
A multidisciplinary team of scientists has developed a new way to detect phase transitions in raw data by analyzing its intrinsic dimension, a statistical property that reveals collective properties of partition functions. This method is agnostic and does not require prior knowledge of the system's parameters.
A new study proposes a modeling framework for V2V and V2I cooperative driving, which prioritizes saving fuel and reducing emissions. The automated algorithm saves energy by 7% under light traffic and 23% under heavy traffic along the corridor.
A recent study published in Information & Management found that human analysis can effectively identify fake online reviews by leveraging linguistic characteristics. Researchers asked participants to distinguish between authentic and fake hotel reviews, with surprisingly effective outcomes.
Researchers at Princeton University have created a new microchip design that enables AI systems to process data closer to the edge of computing infrastructure. The innovative design reduces power consumption and latency, making AI more accessible and efficient.
Researchers at Texas A&M University have developed an algorithm that uses reinforcement learning to automate the prediction of subsurface environments. The algorithm accurately forecasts oil and gas reserves by providing feedback on rewards and penalizing unfavorable predictions.
A new AI algorithm enabled a fully automated artificial pancreas system to achieve improved glucose control without manual input of meal or exercise information. This breakthrough could lead to more efficient and hassle-free diabetes management.
Yingyan Lin, an assistant professor at Rice University, has received a $400,000 NSF CAREER Award to develop more efficient deep learning hardware accelerators. Her goal is to push forward ubiquitous intelligent devices and green artificial intelligence, addressing the gap between complex algorithms and limited resources.
Scientists used fNIRS to analyze brain activity and develop an algorithm to predict autistic traits severity with over 90% accuracy. The new technology is more accessible than fMRI for diagnosing autism spectrum disorders.
A new system called SpAtten enables more streamlined NLP with less computing power, achieving speeds of over 100 times faster than competing general-purpose processors and reducing energy consumption by over 1,000 times.
A recent study found that Google Scholar systematically relegates non-English language documents to invisible positions, even among quality articles with hundreds of citations. This bias can have detrimental effects on researchers from non-English speaking countries, leading them to believe there is no literature in their native language.
Researchers at Geisinger Health System developed an AI algorithm using echocardiogram videos to predict mortality within a year. The model outperformed other clinically used predictors and improved cardiologists' prediction accuracy by 13 percent.
A machine-learning algorithm developed at UT Southwestern estimates that the number of COVID-19 cases in the US is nearly three times that of confirmed cases, with over 71 million people estimated to have contracted the virus. The algorithm provides daily updated estimates of total infections and the number of people currently infected...
Researchers from RUDN University found a way to reduce the size of a trained neural network by six times without retraining, achieving significant storage volume reduction and minimal accuracy loss. The new method leverages correlations between initial and simplified weights, eliminating the need for post-training.
The Ramanujan Machine generates mathematical conjectures without proof, imitating Ramanujan's intuition using AI and computer automation. It has already produced known formulas for pi, Euler's number, and other constants, as well as several unknown conjectures.
Researchers from HSE University developed a new method to process MEG data, enabling cortical activation areas with higher precision. This improvement allows for better diagnosis of neurological disorders and preparation for brain surgery.
Researchers from Universitat Rovira i Virgili and Institute of Materials Science of Barcelona have combined experimental data with algorithms to enable an unprecedented predicting capability of the performance of organic solar cells. The study uses a new experimental method to generate large datasets, which are then used to train machi...
Machine-learning algorithms are trained on large datasets to predict the performance of organic solar cells, identifying key parameters such as electronic gap and charge transport balance. This study demonstrates a new approach to predicting material efficiency, paving the way for further analytical models and more complex system under...
Researchers at Texas A&M University developed a deep-learning algorithm that can denoise images to reveal otherwise invisible details. The algorithm, called global voxel transformer networks (GVTNets), uses adaptive receptive fields to capture information in the overall image structure.
Researchers have detected bias in face recognition algorithms, with higher false positive rates for females with dark skin tone and males with light skin tone. Top winning solutions exceeded 99.9% accuracy, but the analysis of top 10 teams showed that overall accuracy is not enough when building fair face recognition methods.
A new algorithm called Meta-Apo reduces the need for expensive whole-genome sequencing to understand microbial function, improving consistency with 16S rRNA gene amplicon results. This approach enables accurate diagnoses like gingivitis improvement from 65% to 95% using low-cost 16S-amplicon sequencing.
Researchers develop an innovative algorithm inspired by weakly electric fish to detect and locate objects via electrosensing. The multi-scale approach combines information gathered at different distances from the object, providing a more accurate understanding of its features.
Researchers at Università di Trento developed RoomTetris algorithm to efficiently allocate rooms, increasing hotel occupancy rates and profits. The software has shown promising results in tests, with potential to double profitability for some hotels.
Researchers developed fish-inspired robots that synchronize movements in 3D space, exhibiting complex collective behaviors such as aggregation and circle formation. The system uses blue LED lights for vision-based coordination and demonstrates autonomy in underwater environments.
Researchers demonstrate that no single algorithm can determine whether a superintelligent AI would produce harm, and deciding on its intelligence is incomputable. The study's containment problem highlights the challenges of controlling such an AI.
A Sandia Labs research team used machine learning to complete materials science calculations 42,000 times faster than normal, accelerating the creation of new technologies for optics, aerospace, and energy storage.
Researchers at Columbia University are developing a novel platform to enable factory workers to work remotely using a robot teleoperation system. The system will allow workers to specify high-level task goals without expert knowledge of the robot hardware and configuration, enabling greater job access for workers regardless of geograph...
Researchers at the University of Copenhagen find that computer algorithms cannot optimize packing of objects in two dimensions without overlap beyond four or five items. The study's findings have significant implications for industries such as clothing manufacturing and metal processing, where efficient material cutting is crucial.
Researchers use single-cell RNA sequencing to analyze kidney cells' gene expression, enabling the reconstruction of their spatial arrangement and functional information. The algorithm succeeds in completing a 3D puzzle, providing new insights into kidney disease.
Algorithms can adopt collusive pricing rules without human intervention, posing a threat to consumers. A multidisciplinary policy design is proposed to investigate AI pricing algorithms for collusion in controlled environments.
A research team from RUDN University created an algorithm to help large groups of people make optimal decisions quickly. They applied it to a real-life example, where the outbreak of COVID-19 required the administration and sellers to agree on compensation amounts within just three steps.
Researchers developed an AI algorithm called CAMEO that discovered a new compound by operating in a closed loop, maximizing productivity and efficiency. The AI is designed to contain knowledge of key principles, including past simulations and lab experiments, to identify the best material for specific applications.
Scientists are studying the infrasound signatures of tornadoes to develop more accurate prediction and warning systems. Researchers have found that these vibrations can travel long distances quickly and through different media, potentially allowing for early detection of tornadoes from far away.
A team of scientists from Tokyo Institute of Technology devised a strategy to automate the novel material development process using robotics and artificial intelligence. The CASH setup enables fully autonomous materials research, making it possible to test and optimize new compounds at an unprecedented scale.
Researchers developed a computer algorithm that captures traffic network topology to identify congestion hotspots. The algorithm suggests ways around congested areas in cities.
Researchers employed a computer algorithm to analyze nearly 15,000 landscape paintings, finding that compositional structures evolved systematically over time. The study suggests a potential bias in art curators' and historians' selections, contradicting the prevailing view of diverse artistic expression.
A new algorithm helps track fast charged particles in plasma, which could influence fusion reactions. The algorithm conserves energy during pitch-angle scattering, a critical process in fusion plasma.
A new open-source tool, CRYLOGGER, analyzes thousands of Android apps to detect cryptographic misuses without requiring access to the app's code. The study found that nearly all popular Android apps contain code or use libraries that do not strictly adhere to security standards.
A new machine learning algorithm has been developed to isolate dynamic neural patterns in brain signals that relate to specific behaviors, such as finger movements. The algorithm resolves the challenge of isolating these patterns, which can be masked by other activities, and enhances the decoding of behaviors from brain signals.
Researchers from the University of Michigan found that the vast majority of field massive stars in the Small Magellanic Cloud are 'runaways,' or stars ejected from clusters. They discovered that these stars could have formed in isolation or were dynamically ejected due to unstable orbital configurations.
Purdue University is leading a $3.7 million research project to create more secure machine learning algorithms for autonomous systems. The goal is to develop a robust, distributed and usable software suite to prevent AI hacking and ensure the accuracy of autonomous machines on the battlefield.
A new algorithm could help reduce stress on health systems in the UK and beyond by redistributing ICU patients across hospitals. The load balancing method, developed by Queen Mary University of London, has been shown to enable access for up to 1000 additional cases in the UK.
A team of researchers from Rensselaer Polytechnic Institute developed a deep neural network that can perform nearly as well as more complex dual-energy CT imaging technology. The algorithm produced high-quality approximations with a relative error of less than 2% using single-spectrum CT data.
Researchers found that AI techniques are being used to process information about brain structure and connectivity, assess surgical candidacy, and predict disease trajectory. However, they emphasize the need for explainable algorithms to ensure trust in algorithmic prescriptions or diagnoses.
Researchers created an abstract language that describes protein molecules' shapes and structures, enabling predictions of their dynamics. This method uses machine learning algorithms to analyze molecular movements and provides insights into disease causes and targeted drug therapies.
Researchers developed an algorithm to predict sex-specific adverse drug effects, identifying 20,817 adverse drug effects posing sex-specific risks. The algorithm addresses confounding biases and provides an opportunity to minimize adverse events by tailoring drug prescription and dosage to sex.
Skoltech researchers propose a fast and accurate numerical method to address the low spatial resolution of EEG studies. The new approach directly backpropagates measured signals from the skin down to the cortex, increasing accuracy for source localization while speeding up processing.
Cyber-physical systems face challenges due to large volumes of data flowing through communication networks, causing routing and queuing delays that degrade system quality. The new algorithms developed by researchers strike a balance between communication sparsity, delay, and performance, ensuring safe and stable operation.
The University of Texas at Austin has been selected to lead the NSF AI Institute for Foundations of Machine Learning, aiming to develop new classes of algorithms for more sophisticated AI technologies. The institute will focus on addressing challenges such as noise and diffuseness in modern datasets.