Articles tagged with Algorithms
An international team of researchers developed a new computational method to predict the functions of thousands of microbial genes. The method, based on machine learning algorithms, analyzes 'big data' from human microbiomes and other environments to identify evolutionary signals that can assign biological roles to unknown genes.
A researcher at MSU developed an algorithm to improve information security tools by reducing the complexity of scalar multiplication in elliptical curve transformations. The new algorithm achieves a 5% reduction in precomputation stage complexity and a 4% reduction in main stage complexity.
The researchers developed a novel synaptic architecture that could lead to a new class of information processing systems inspired by the brain. Prototype chips containing over one million nanoscale memristive devices were used to implement a neural network for detecting hidden patterns and correlations in time-varying signals.
The Cheetah 3 robot can navigate staircases littered with debris and recover its balance when yanked or shoved, thanks to two new algorithms developed by MIT engineers. The contact detection algorithm helps the robot determine the best time for a leg to switch from swinging in the air to stepping on the ground.
Researchers at the Higher School of Economics have developed a new method for recognizing people on video using only one photo, achieving higher recognition accuracy compared to existing methods. The algorithm uses information on how reference photos are related to correct errors in video frame recognition.
Computer scientists at Harvard SEAS developed a new algorithm that exponentially speeds up computation by reducing parallel steps required to reach a solution. The algorithm samples directions in parallel and discards low-value directions, enabling real-world summarization processes to be developed at unprecedented scale.
Researchers developed an algorithm that accelerates the registration of 3D medical images by learning from previous registrations, reducing processing time to minutes or seconds. The 'VoxelMorph' algorithm uses convolutional neural networks and achieves comparable accuracy to state-of-the-art systems.
Researchers at Princeton University have improved a proteomics method to accurately count proteins in cells under different circumstances. The new approach, TMTc+, uses a combination of cell sample preparation and computer algorithm changes to provide superior measurement accuracy and precision compared to existing methods.
A new algorithm developed by researchers at UCLA Medical Center can predict hypotension in surgical patients with high accuracy, enabling physicians to take proactive measures. The algorithm uses machine learning to analyze physiological data and identifies subtle signs of impending hypotension, reducing the risk of serious complications.
Researchers developed an algorithm to determine the optimal caffeine dosage and timing for maximum alertness during sleep loss. The 2B-Alert app uses a validated mathematical model and optimization algorithm to provide personalized caffeine-dosing strategies, improving alertness by up to 64%.
Researchers at MIT developed an algorithm to prioritize and transmit data from sensors in real-time, ensuring the freshest possible data is received by a network. The algorithm calculates an 'index' based on data age, channel reliability, and node priority, guaranteeing optimal decision-making without overloading wireless channels.
Researchers from Rice University and Duke University developed a new method to accurately estimate the number of identified victims killed in the Syrian civil war. Using 'hashing with statistical estimation,' they produced real-time estimates with a lower margin of error than existing methods.
A newly developed algorithm determines ideal caffeine dosage and timing for optimal alertness. The algorithm improves neurobehavioral performance by up to 64% using the same total amount of caffeine, or reduces caffeine consumption by up to 65% while maintaining equivalent improvements in alertness.
Researchers developed a new algorithm, GDP-ADMM, to further enhance the capabilities of SHARP in reconstructing high-resolution images from ptychographic datasets. The new framework takes advantage of state-of-the-art mathematical aspects to improve data acquisition and image resolution.
Scientists developed a machine learning approach to predict microbial pathways, allowing for faster design and development of biofuels. The method accurately predicted biofuel production profiles, outperforming traditional kinetic models.
Researchers at St. Jude Children's Research Hospital have identified a hidden driver, kinases Mst1 and Mst2, that regulates the function of different dendritic cell subsets and primes anti-tumor T cells. This discovery provides clues for new treatment strategies by modulating dendritic cell activity to shape the immune response.
The Swiss State Secretariat for Migration and the Immigration Policy Lab (IPL) are testing a new data-driven method to assign asylum seekers to cantons across Switzerland. The algorithm aims to maximize job chances, and its recommendations will be compared to those of randomly allocated individuals over several years.
A NIST study reveals that facial recognition experts achieve maximum accuracy when working with artificial intelligence, rather than another human. Trained professionals outperformed untrained control groups and algorithms performed comparable to top-performers.
A study by Cornell University and MIT offers a network-based solution to the minimum fleet problem, proposing a 30-40% reduction in taxi fleet size. The research uses a matching formula to optimize ride requests and vehicle dispatching, assuming a top-down model.
A new algorithm developed by Tufts University researchers enables the self-localization of IoT devices on 5G networks, eliminating the need for centralized anchors and improving location-based services. This scalable solution can meet the demands of a projected 50 billion connected products by 2020.
Researchers at MIT have developed a fleet of autonomous boats that can ferry goods and people, reducing street traffic. The boats can also be printed using 3D technology and equipped with sensors to monitor water quality, providing insights into urban health.
A new lane-change algorithm developed by MIT allows autonomous vehicles to model an entire range of driving styles, from conservative to aggressive, with safety guarantees. The system computes buffer zones on the fly and ensures collision avoidance within a given time frame.
Researchers developed a computational way of analyzing X-ray images of lungs to assess Chronic Obstructive Pulmonary Disease (COPD) and other lung diseases. The new method uses topology, combining CT scans, high-performance computing, and algorithms to identify lung function characteristics.
HRL Laboratories joins DARPA's Assured Autonomy program with the Expressive Assurance Case Toolkit (ExACT) to ensure autonomous vehicle systems perform as programmed without unsafe behavior. The tool kit mathematically verifies algorithms lead to safe and reliable system behavior, considering physics and dynamics of the system.
A new open-access software called PyFRAP has been developed to accurately analyze molecular diffusion in living cells. The program takes into account three-dimensional structures and provides reliable results, especially under complicated conditions.
Daniel Sheldon's NSF grant aims to develop efficient algorithms for complex models, enabling scientists to make predictions and test hypotheses from large datasets. He plans to address challenges in modeling animal populations, causal reasoning about citizen scientist data, and optimizing decision-making with multiple objectives.
A study by Columbia University researchers found that social networking site Instagram's recommendation algorithms amplify homophily, making women less visible and reducing their popularity. The algorithms, which turn loose on a network with homophily, effectively make women less visible.
Researchers investigate how algorithms create personalized content and 'filter bubbles' that can foster biases and inequality. The MyBubble project aims to provide transparency into online advertising and profiling.
Researchers at University at Buffalo School of Management developed a new algorithm to establish pricing and allocate resources for cloud computing. The study provides practical formulas that companies can use to determine necessary resources and set charges for their customers.
A new method developed by researchers at USC Viterbi School of Engineering uses machine learning to measure key risk factor for cardiovascular diseases and arterial stiffness using just a smartphone. The method was validated with existing tonometry data and showed high correlation with actual tonometry measurements.
Researchers at UC Berkeley have developed a method using deep reinforcement learning to create realistic computer animations. The system, dubbed DeepMimic, can simulate various skills, including acrobatic feats like break dancing and martial arts, and respond naturally to changes in the environment.
A new 'smart' algorithm is proposed to manage household energy usage, taking into account varying power sources like windmills and solar panels. The goal is to minimize costs while meeting energy demand, with the potential for future optimization through machine learning.
A study using machine-learning algorithms and MRI analysis predicts the severity of neurologic impairment after spinal cord injury. The results show variable accuracy in classifying patients and identifying potential prognostic texture features.
Scientists from Spain have created a new technique using magnetic resonance imaging (MRI) to evaluate the properties of Iberian hams and whole loins. This non-invasive method allows for the prediction of quality characteristics such as fat content, moisture levels, color, and sensory attributes without destroying the meat products.
Researchers created an algorithm that sorts intervention program participants into smaller groups based on social connections and prior history of substance abuse. The AI tool performs significantly better than control strategies in forming groups, suggesting that evenly distributing regular users can decrease success rates.
A new study by UTSA researchers presents a method to stop cyber attacks on GPS-enabled devices, including electrical grids and driverless cars. The algorithm can recognize false GPS signals and counter an attack in real-time, making it crucial for securing GPS signals used in various technologies.
Researchers at UPV/EHU developed an algorithm to calculate chest compression depth and frequency based on chest acceleration, validating its accuracy in real-time monitoring of CPR quality. A new device is being marketed to assist CPR, aiming to improve patient survival rates.
The eGVHD App is a user-friendly tool that supports accurate GvHD diagnosis and scoring after HCT. It provides guidelines, definitions, and pictures to help clinicians evaluate all potentially affected organ systems.
The use of artificial intelligence in medicine raises important ethical questions, including bias in algorithms and the potential for data to replace human clinical experience. Researchers call for careful examination of these risks and for physicians to critically assess machine-learning tools and their limitations.
Researchers at U.S. Army Research Laboratory have developed a way to factor large composite integers using brain-inspired computer architectures, moving away from traditional computing methods. This breakthrough could break many modern-day internet security protocols, including public key encryption.
A field-data study in Los Angeles found that predictive policing did not result in biased arrests, with arrest rates by ethnic group showing no statistically significant difference. However, the researchers noted that higher crime rates and proportionally higher arrest rates were observed in algorithmically selected areas.
A team of researchers has developed an algorithm that uses GPS tracking and data analytics to optimize the harvesting of hand-picked crops. By analyzing the movement of each worker in real-time, the algorithm can predict the amount of completed boxes and improve overall efficiency.
The study, led by George Mohler, found no statistically significant difference in arrest rates by ethnic group between predictive policing and standard patrol practices. The researchers also discovered that arrests were higher in algorithmically-selected areas but remained unchanged when adjusted for crime rates.
Researchers at Lawrence Berkeley National Laboratory developed HipMCL, an algorithm that can cluster large biological networks containing millions of nodes and edges. The new method allows biologists to make sense of big science data using massively parallel supercomputers.
A new study has developed an automated approach to select and seed teams for the NCAA March Madness basketball tournaments, providing an objective and unbiased starting point. The algorithm achieved high accuracy rates, matching 24 of 38 top teams in seeding and 89.6% being within two seeds of committee rankings.
A new study presents a machine learning algorithm that accurately predicts heart attack diagnoses with a 94% accuracy rate, outperforming standard clinical models. The algorithm uses large collections of patient data to identify patterns and improve routine processes in healthcare delivery.
A USC-developed algorithm can better locate and treat people living with undiagnosed infectious diseases, such as TB and gonorrhea. The algorithm optimizes disease spread models using behavioral, demographic, and epidemic trends to capture underlying population dynamics.
A team from Lehigh University's Industrial and Systems Engineering department participated in a workshop on big data optimization algorithms, theory, applications and systems. Researchers presented their work on Interior Point Methods, machine learning methodologies, and other topics relevant to big data analytics.
NanoMap enables drones to fly through cluttered spaces like cities at high speeds using a simple yet effective approach that considers the drone's position in the world over time. The system reduces crash rates from over 90% to just 2%, making it suitable for applications such as search-and-rescue, defense, and package delivery.
Researchers at USC Center for Artificial Intelligence in Society developed an AI algorithm, SPOT, to detect poachers in near real-time using infrared images. The algorithm can distinguish between humans and animals in just over three-tenths of a second, reducing the time spent on monitoring wildlife.
Researchers developed DroNet, an algorithm that enables drones to safely navigate through urban environments using a normal camera and powerful artificial intelligence. The drones learned to respect safety rules and adapt to new environments, paving the way for fully autonomous drones in various applications.
The Northwestern University team developed highly efficient metadevices at millimeter-wave frequencies using inverse design principles and 3D printing. This approach starts with a function and asks what structure is needed to achieve the desired result, producing unexpected outcomes like broad bandwidth functionality.
A team of computer scientists developed an algorithm called VarQuest to rapidly search massive databases for new antibiotic variants, discovering over a thousand new variants.
A new algorithm developed by Stanford researchers can help resettle refugees more effectively, improving their employment success and overall integration. The algorithm analyzes historical data on refugee resettlement and assigns placements that project a 40-70% increase in employment rates compared to actual outcomes.
A recent study by Brigham Young University researchers found that machines trained with an algorithm called S# outperformed humans in finding compromises that benefit both parties. The machines were able to learn from 'cheap talk' phrases and maintain cooperation, often more effectively than humans.
A new algorithm developed by Stanford University's Immigration Policy Lab can optimize refugee resettlement, leading to improved employment and integration rates. The algorithm takes into account individual characteristics and local conditions to match refugees with the most suitable locations.
A Dartmouth College study found that non-experts who responded to an online survey performed equally as well as the Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) software system in predicting repeat criminal behavior. The study also demonstrated that only two variables - a defendant's age and number of p...
Researchers used digital pathology slide images to train computer algorithms that could detect the spread of breast cancer to lymph nodes with high accuracy. The study found that AI algorithms outperformed human pathologists in detecting cancer spread, suggesting potential improvements in diagnosis and treatment outcomes.
A new recommendation algorithm uses cosine similarity to predict customer preferences in high-dimensional spaces, leveraging the angle between user rating lines. The approach balances signal gain with noise introduction, resulting in more accurate predictions and better performance, particularly when ratings data is sparse.
A new algorithm can speed up protein-folding simulations, allowing researchers to model phenomena that were previously out of reach. This technique can help scientists better understand and treat diseases like Alzheimer's, which is associated with amyloid-beta protein fragments forming hard plaques that disrupt neurons.