Articles tagged with Algorithms
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.
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.
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 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.
A new algorithm, developed by a team at the University of Maryland, uses artificial neural networks to address multiple flaws in a single image. The algorithm can be trained on high-quality images and then applied to any image with imperfections.
The Meta Planning Engine system detects changes in demand and uses multiple algorithms to find the best solution for traffic management. It provides flexibility and adaptability, allowing the software to choose the optimal algorithm for each situation.
Researchers at Lobachevsky University have developed a mathematical model to explain cognitive dissonance through the lens of information images. The theory posits that there is a limited space filled with interacting information images, governed by laws that determine real and virtual activities.
A Worcester Polytechnic Institute researcher has developed algorithms to identify fake likes, followers, and posts on various platforms with high accuracy. The goal is to prevent crowdturfing, which can spread misinformation and undermine online credibility.
Researchers developed Fastron, a machine learning-based collision detection algorithm that runs up to 8 times faster than existing methods. The algorithm uses a minimalistic approach to classify collisions versus non-collisions in dynamic environments.
Researchers at Salk Institute have found that fruit fly brains use an efficient method to perform similarity searches, expanding the dimension of odor information to improve detection. This approach could inform computer algorithms and enhance their ability to find similarities quickly.
A researcher at the University of Kansas is working on developing a foundational mathematical framework to account for unstructured uncertainty in complex dynamic systems. The goal is to improve predictive accuracy in fields such as engineering, nature, and global climate change.
A team of scientists and amateur astronomers have detected six exocomets, the smallest objects yet found outside our solar system, using transit photometry. The detection marks the first time an object as small as a comet has been identified using this technique.
A new computer model, Recursive Cortical Network (RCN), has been developed that can solve CAPTCHA and identify handwritten digits with comparable accuracy to state-of-the-art deep learning approaches but using significantly less training data.
Researchers from University of Leicester and KU Leuven found that information flow accounts for emergence of small-world networks in complex systems. These structures arise spontaneously in neuronal and social networks, and are characterized by short-cuts and hierarchical organization.
Computer scientist Jelani Nelson finds Johnson-Lindenstrauss lemma best approach to reduce data dimensionality. The theorem helps speed up algorithms across various fields.
Researchers use angular correlations of scattered X-rays to determine the 3D structure of biological objects, overcoming limitations of traditional X-ray methods. The breakthrough enables scientists to study molecular structure and dynamics previously impossible to observe.
Scientists discovered a network of neurons in bees' brains that integrates direction and distance changes, enabling them to return home. A detailed computer model was developed based on this research, which could lead to new algorithms for autonomous robots.
Researchers have discovered a potential method to predict nearby strong earthquakes by analyzing deep tremors. The study, published in the Journal of Geophysical Research: Solid Earth, found that changes in deep tremor patterns can signal an impending earthquake.
Chinese scientists develop a new algorithm that leverages network structure characteristics to improve link prediction accuracy and robustness. Their experimental testing in various real-world networks yields better results than existing methods, leading to the creation of a novel method for predicting missing links.
Four primary methods are used by primary care practices to risk stratify patient populations: practice-developed algorithm, AAFP clinical algorithm, payer claims/electronic health record, and clinical intuition. Practices that developed their own algorithm identify more high-risk patients than those using other methods.
Researchers developed an algorithm to generate contiguous district shapes, balancing population and political affiliation. The tool enables state legislatures to explore options in a transparent and efficient manner, promoting fairness and reducing partisan biases.
The system uses cameras and AI to detect hand movements deviating from normal driving behavior, grading them for possible safety threats. Researchers hope the technology will reduce traffic accidents caused by distracted drivers, a major contributor to global crashes.
A computer algorithm developed by ITMO University's programmers analyzes Instagram photos taken in Saint Petersburg to identify popular locations favored by residents. The results provide insider information for tourists, offering a more authentic experience.
A novel brain-computer interface (BCI) has been developed to improve motor function in stroke patients. The BCI produced a 36% improvement in motor function of a stroke-damaged hand after just 10 training sessions. This innovative therapy has shown promise for helping stroke patients recover from debilitating damage.
Researchers developed a novel cartilage degeneration algorithm to predict the progression of osteoarthritis. The algorithm shows great potential in patient-specific progression prediction, and may facilitate clinical decision-making in treatment.
Shaden Smith and Yang You have been awarded prestigious fellowships for their work on high-performance computing applications. They are recognized for advancing the state of the art in sparse tensor factorization algorithms and scalable machine learning algorithms.
A team of scientists from the University of Freiburg has created a self-learning algorithm that decodes human brain signals measured by an electroencephalogram (EEG) with high accuracy. The algorithm, based on brain-inspired models, can recognize and differentiate between various behavioral patterns from different movements, making it ...
Researchers from NIST and Michigan State University have developed an algorithm that automates the key decision point in fingerprint analysis, reducing human subjectivity and improving reliability. The new system uses machine learning to score latent prints based on their quality, allowing for more efficient processing of evidence.
A team of researchers has created a three-dimensional movie showing a virus preparing to infect a healthy cell. The study uses powerful algorithms and X-ray imaging equipment to reveal the sequential images of the virus's molecular machinery. The findings provide new insights into how viruses undergo changes during infection.
A team of researchers developed a machine-learning algorithm to identify counterfeit products by analyzing microscopic characteristics. The system achieved an accuracy rate of over 98%, enabling non-invasive verification of product authenticity.
A BYU research team has developed a machine learning approach to analyze grain boundaries in metals, enabling the prediction of material strength and corrosion resistance. By analyzing massive data sets, their algorithm provides insight into physical structures associated with specific mechanisms and properties.
Researchers used modularity metric to analyze brain connectivity in 90 stroke patients, finding that highly fragmented left hemisphere communities were associated with more severe aphasia. The study provides new insights into the underlying mechanisms of language recovery after stroke.
A smartphone app developed by computer scientists at the University of Waterloo uses an algorithm to direct users on camera positioning for optimal photos. The app, which has shown a 26% improvement in selfie quality, teaches users about composition principles and can be expanded to include additional factors.
Researchers developed an algorithm to identify the stress-induced breakdown of molecular bonds, enabling efficient chemical synthesis and catalysis. The algorithm can be applied to any molecule, including biological ones, and has implications for various applications such as molecular machines and catalyst design.
FPGAs can now handle five times more calculations, saving industry huge sums and enabling new functionality without hardware replacement. Carl Ingemarsson's method optimizes signal routes in chips to achieve the boost.
Researchers at University of Seville developed an algorithm to estimate walk time based on length and gradient, achieving better results than current systems. The new method has practical applications for route management, emergency response, and urban planning.
Researchers at Lawrence Berkeley National Laboratory develop Multi-Tiered Iterative Phasing (M-TIP) algorithm to determine molecular structure from sparse and noisy single-particle diffraction data. This approach reduces the amount of required information, enabling the extraction of more features from limited experiments.
A study using computer vision algorithms examines millions of Google Street View images to measure urban change, finding that high density and education are key drivers of improvement. The research also supports three classical theories of urban change, highlighting the importance of human capital and education in shaping cities.
Researchers developed a model to explain human moral behavior in simulated scenarios, showing that machines can adopt human-like moral decision-making. The study has significant implications for self-driving cars and other autonomous systems, sparking debate about the role of morality in machine behavior.
Researchers matched would-be carpoolers with people driving to work based on social media data and found that compatibility increased satisfaction and reduced car use by 57% in Rome and 40% in San Francisco. This approach could lead to a dent in gridlock, reduce pollution and make commuting more enjoyable.
A universal algorithm for folding origami shapes guarantees a minimum number of seams, producing more practical and sturdy structures. The new method preserves the boundaries of the original piece of paper, allowing users to choose where seams meet.
Researchers at Numenta have developed an online sequence memory algorithm called Hierarchical Temporal Memory (HTM) that can detect anomalies in real-time streaming data without supervision. The technique is based on the principles of how the brain works and has been tested using the Numenta Anomaly Benchmark.
New simulations reveal DNA's constant motion enables rapid transcription factor diffusion, contradicting the long-held assumption of rigid DNA. The findings have significant implications for understanding cell processes and potentially boost speed and accuracy in biological and medical research.
A new method using microphones has been developed to track pollinating bees, which could save farms money by predicting bee activity and pollination services. The researchers recorded bee buzzing sounds using inexpensive equipment and compared them to visual counts, achieving high accuracy.
A recent Stanford University School of Medicine study found that fitness trackers generally accurately measure heart rate but struggle with calculating energy expenditure, which is often used to track calories burned. The study evaluated seven devices and found that six were accurate in measuring heart rate within 5% error, while none ...
Researchers at Duke University have developed new algorithms that enable analysis of sensitive data while guaranteeing individual privacy. The tools use a customized definition of privacy similar to differential privacy, injecting just enough noise to satisfy regulations and uphold the law.
A NJIT graduate student has won an IBM fellowship to develop computer systems that mimic the human brain's architecture. The goal is to raise computers' learning and inference capabilities while lowering energy consumption.
Researchers developed a personalized algorithm that predicts the impact of particular foods on an individual's blood sugar levels. The Glucoracle app allows users to upload food and blood measurements, providing real-time predictions of post-meal blood sugar levels.
Joachim Weickert's new technique uses a phenomenon from the heat equation to store only a few important pixels, achieving excellent compression ratios and potentially surpassing established methods like JPEG. The challenge now is to determine the ideal combination of pixels for storage while developing more efficient algorithms.
A study published in PLOS Computational Biology found that humans' ability to make random choices peaks around age 25 and declines thereafter. The researchers assessed over 3,400 participants and used online tasks to evaluate their algorithmic randomness.