Articles tagged with Algorithms
A new study proposes using standard compression algorithms like zip software to calculate entropy, providing insights into protein folding and other complex systems. This method has endless applications in fields such as biomedical simulations and basic research.
Economists propose a novel approach to measuring inequality, accounting for individual relationships and social interactions. The new method correctly calculates the Gini coefficient by considering differences between individuals' wealth, not just individual values.
Researchers at Stanford and UMass Amherst develop a new technique to create machine-learning algorithms that can learn to avoid undesirable outcomes such as gender bias and excessive risk. Their approach, called the Seldonian algorithm, enables users to specify what behaviors they want an AI system to avoid with high probability.
A new framework for designing machine learning algorithms helps prevent undesirable behavior, including biases in medical diagnoses and financial predictions. The approach shifts the burden from users to designers, making it easier to create 'well-behaved' algorithms without requiring extensive domain knowledge or complex data analysis.
Researchers at Stevens Institute of Technology have completed the first census of Wikipedia's 1,601 bots, shedding light on their functions and interactions with human users. The study found that bots play nine core roles, including fixers, connectors, and protectors, which together account for about 10% of all activity on the site.
Researchers have created a spatial map of gene expression for individual cells in various tissues, including the liver and intestinal epithelium. The new algorithm, called 'novoSpaRc', uses machine learning to track gene activity and reveals new insights into tissue organization and regulation.
Researchers found that hiring algorithms are often opaque and biased, with few vendors disclosing concrete information on validation and mitigation. The study encourages transparency and conversation around ethical decision-making in pre-employment assessments using machine learning.
Researchers at Ludwig-Maximilians-Universität München have developed a new workflow for producing formaldehyde that increases yields by a factor of five, making it more efficient and sustainable than traditional methods. This breakthrough could significantly contribute to climate mitigation, with annual global production exceeding 20 m...
Researchers at Stevens Institute of Technology developed a meal-detection system that can accurately calculate carbohydrate intake in real-time, allowing for faster insulin administration. The system's estimates are remarkably accurate, outperforming previous systems by up to 17 grams.
A new co-regulation map of the human proteome has been created, enabling the prediction and assignment of functions to uncharacterized human proteins. The map reveals unexpected partnerships between proteins, including peroxisomal membrane protein PEX11β with mitochondrial respiration factors.
A team of international researchers has developed a new control algorithm that reduces the complexity of bilateral teleoperation systems while maintaining their performance. The proposed composited stated convergence scheme achieves this by reducing communication channels, resulting in better transient performance.
Researchers developed a compact depth sensor inspired by jumping spiders' impressive depth perception. The metalens sensor combines with an ultra-efficient algorithm to measure depth in a single shot, enabling applications in microrobots, small wearable devices, and virtual/augmented reality headsets.
A new study from UC Berkeley and Chicago Booth reveals significant racial bias in software programs that determine access to high-risk healthcare management programs. Fixing this bias could more than double the number of black patients automatically admitted to these programs.
Researchers developed an algorithm to recommend tags for social media posts, resulting in a 20% boost in popularity. The system takes into account user popularity and content emotional impressions to suggest effective tags.
A new study from the University of Illinois introduces a tool that helps quantify the connection between traffic accidents and city road networks. Researchers found that commuters willing to take longer trips can reduce accidents by up to 18%.
Developed by Chung-Ang University researchers, the new exosuit helps users walk and run more efficiently while reducing energy expenditure. It features a fabric vest, wires, and batteries, weighing just 5kg and automatically switching between walking and running modes.
A team of researchers at KAUST has developed a mathematical bridge between high-level symbolic representations and statistical data, enabling artificial intelligence algorithms to make use of vast stores of symbolic knowledge. This breakthrough could lead to significant improvements in machine learning and biomedical applications.
Researchers at MIT have created an algorithm that significantly speeds up the planning process required for robots to adjust their grasp on objects. The new approach uses motion cones to efficiently calculate feasible pushes and reposition objects in less than a second, compared to traditional algorithms that take over 500 seconds.
Researchers at Iowa State University have developed a new algorithm to solve the inverse chirp z-transform (ICZT), a problem that had gone unsolved for 50 years. The algorithm has been tested for numerical accuracy and shows promising results in terms of computational complexity.
The University of New Mexico-led project, Cognitive Autonomy for Human CPS: Turning Novices into Experts, seeks to develop a framework for improving autonomous technology's adaptability to real-life human behavior. The team will investigate ways to anticipate and mitigate common human-automation interaction problems.
Researchers at Duke University use machine learning to model complex biological circuits, achieving speeds of hours instead of years or months. By training a deep neural network on large datasets, they uncover patterns and interactions between variables that were previously impossible to discover.
Researchers at UCSB have enabled identifying a person behind a wall using only WiFi transceivers outside, utilizing received power measurements of a WiFi link. The proposed approach achieves an accuracy of 84% in correctly identifying the person behind the wall, with potential applications in surveillance and security.
A new algorithm developed by MIT student Trevor Henderson seamlessly blends different audio signals in real-time, recreating the portamento effect. The algorithm uses optimal transport to map pitches from one signal to another, producing a smooth transition without volume fading.
Researchers at Queen Mary University of London used AI to analyze 17,000 healthy UK Biobank volunteers' heart MRI images, finding genetic factors account for 22-39% variation in left ventricle size and function. The study identified 14 genome regions associated with heart structure and function.
Duke University engineers used machine learning to design dielectric metamaterials that absorb and emit specific frequencies of terahertz radiation, reducing calculation time from over 2,000 years to just 23 hours. The new designs enable thermophotovoltaic devices that convert waste heat to electricity with higher efficiency.
A study found that cardiovascular disease risk prediction algorithms may underestimate the risk for people with severe mental illness, often defined as schizophrenia, bipolar disorder, and major depression. The algorithm consistently underestimated cardiovascular disease rates in this group, particularly in women.
Researchers at the University of Bern have been awarded prestigious starting grants for their innovative projects on digital 3D models and fluid-rock interactions. The €1.5 million AlgoHex project aims to develop algorithms for automatic hexahedral mesh generation, while the €2 million PROMOTING project seeks to model fluid pathways in...
A three-year grant of 1.2 million rubles will support PhD candidate Angelina Markina's work on efficient algorithms for microstrip antenna design. The project aims to develop mathematical models and prototypes for various frequencies, enabling multi-bandwidth applications including 5G.
Researchers at USC Institute for Creative Technologies found that spontaneous facial expressions are often context-dependent and do not accurately reveal intentions. The study challenges the assumption that facial expressions can be used to predict feelings and actions.
A study by Karolinska Institutet shows that a mobile phone-based treatment adjustment algorithm can improve symptoms and medication adherence in uncontrolled asthma sufferers. The digital tool, AsthmaTuner, analyzes lung function and symptoms to provide personalized feedback and treatment recommendations.
A study of 3.5 million books found that words describing women tend to focus on physical appearance, while those describing men refer to behavior and personal qualities. The analysis highlights the potential impact of biased language on AI systems and employee recommendations.
A new algorithm developed by University of Waterloo researchers increases the efficiency of solar photovoltaic systems and reduces power waste, with potential savings of up to 138.9 kWh/year for small home-use systems. The technique could lead to substantial reductions in emissions from large-scale solar farms.
A team of scientists at EPFL developed an algorithm that can estimate a microscope's resolution from a single image, boosting image quality and enabling optimized imaging conditions. The algorithm has been made available as an open-source plugin, allowing researchers to directly obtain the estimate and optimize their microscopes.
A WPI mathematician is working on a project to develop methods to detect flaws in carbon nanotube materials used in composite rocket fuel tanks. He has developed an algorithm that increases the resolution of density scanning systems nine times, enabling more accurate images of the material's uniformity.
Researchers at the University of Washington have developed an algorithm that can monitor factory and warehouse workers' behavior in real-time, providing risk scores and suggestions for improving ergonomics. The app will alert workers to high-risk actions and provide feedback on how to modify their behaviors.
A team of scientists at Bar-Ilan University has developed a new type of ultrafast artificial intelligence algorithm based on the slow dynamics of brain function. This breakthrough outperforms traditional machine learning algorithms in various fields.
University of Utah chemists developed an algorithm that analyzes previous chemical reaction data to predict hypothetical reactions, narrowing the range of conditions needed for successful synthesis. The model successfully predicted outcomes for various reactions, offering a time-saving solution for pharmaceutical and materials research.
A team of UCI researchers developed a deep reinforcement learning algorithm called DeepCubeA, which can solve the Rubik's Cube in under 20 moves, outperforming human solvers. The algorithm works on other combinatorial games and demonstrates symbolic, mathematical, and abstract thinking capabilities.
A Carnegie Mellon AI program called Pluribus defeated leading poker professionals in six-player no-limit Texas hold'em, achieving superhuman performance. It developed unique strategies such as frequent donk bets and mixed play, which human pros struggled to execute.
Researchers use Word2vec to analyze relationships between words in scientific literature, predicting discoveries of new thermoelectric materials years in advance. The algorithm learns concepts like the periodic table and crystal structure of metals without human guidance.
A new AI tool called EternaBrain uses a neural network approach to predict the choices of top players in an internet-based videogame. The researchers discovered that EternaBrain outperforms random guessing and performs similarly or better than previously developed algorithms.
Cornell researchers develop algorithm to visualize models of the universe, unlocking secrets of cosmology and dark matter. The algorithm uses intensive principal component analysis to extract patterns from large datasets, providing new insights into the nature of our universe.
Researchers at MIT develop a new algorithm that can accurately pick out an object, such as a small animal, in a dense cloud of dots within seconds. The technique prunes away outliers quickly, even for increasingly dense clouds, making it suitable for applications like driverless cars and robotic assistants.
A new AI-powered tool can identify triatomine bugs, also known as 'kissing bugs', that spread the incurable disease Chagas with high accuracy, allowing public health officials to target prevention efforts. The algorithm achieved 95.8% and 98.9% correct identification rates for Mexican and Brazilian species, respectively.
Researchers improve consensus in Proof of Stake protocols by introducing weighted voting schemes, ensuring fairness and security. The new approach scales votes according to validators' profiles without interfering with existing PoS execution.
A Massachusetts Institute of Technology-developed system automatically generates optimized cryptography code for Google Chrome browsers and web applications, matching performance of handwritten code but faster. The system, known as Fiat Cryptography, is being widely used by tech firms and has populated Google's BoringSSL library.
A new AI tool developed by researchers at Stanford University improves clinicians' ability to correctly identify brain aneurysms by highlighting areas of interest on scans. The HeadXNet algorithm reduces the 'miss' rate and increases consensus among clinicians, with promising results but further investigation needed.
A new microgrid system designed by American and Chinese researchers promises improved stability, safety, and resilience in delivering energy. The system aligns several energy sources in parallel and uses a decentralized control algorithm to overcome the burdens of system overload and shutdown.
Researchers at RIT are developing a computational framework to reconstruct and visualize myocardial active stress, enabling direct appraisal of cardiac function. This will help detect and localize regions with diminished contractile activity, improving diagnosis and treatment of cardiac disease.
Researchers develop a new algorithm using over 230,000 patient records to predict mortality risk and survival chances. The algorithm uses disease history from the past 23 years to make more accurate predictions than existing methods.
Researchers at Pitt and CMU aim to develop AI-powered robotic suits that can treat and stabilize soldiers in the field, extending the 'golden hour' for medical evacuations. The project uses real-world data and medical algorithms to guide critical care interventions.
USC researchers Bhaskar Krishnamachari and Aditya Asgaonkar propose a blockchain-based solution to reduce costs and increase efficiency in online transactions. Their system allows buyers and sellers to interact directly without third-party mediators, using smart contracts to verify honesty and disincentivize malicious behavior.
A new brain-controlled hearing aid technology developed by Columbia engineers can identify and amplify the correct speaker in a crowded environment. The device uses artificial intelligence to monitor wearers' brain waves and boost the voice they want to focus on, solving the 'cocktail party problem' that modern hearing aids struggle with.
A new algorithm called DeeProtein uses sensitivity analysis to unravel the secret of its predictions, providing valuable insights into protein functions. This technique enables researchers to identify critical regions in proteins that tolerate changes well or poorly, paving the way for targeted modifications.
A machine learning algorithm can detect signs of anxiety and depression in young children's speech, potentially providing a fast and easy way to diagnose conditions that are difficult to spot. The algorithm is highly successful at diagnosing children with an internalizing disorder with 80% accuracy.
Researchers developed an algorithm called Scanorama that merges over 20 diverse human cell datasets into a single, diverse source of data. The algorithm uses a modified computer-vision technique to find matching cells across datasets and preserves unique cell types.
A team of researchers has developed a mathematical model to calculate the cost - time and energy - to complete a task based on the number of drones and recharging stations available. The model considers the energy required for each drone to complete its portion of the task and fly to a charging station as needed.
Researchers have developed an energy renormalization algorithm to predict glass' mechanical behavior at varying temperatures. This approach enables the design of dynamic materials with optimal properties, scaling molecular simulations up by roughly a thousand times.
Researchers at Rutgers University have developed a low-cost, high-speed algorithm to test chemical toxicity without animals. The algorithm uses massive amounts of data from PubChem and achieves a 62-100% success rate in predicting oral toxicity for several groups of chemicals.
Researchers propose a unified approach to optimize radar polarimetry data for precise weather forecasting. This combines observation-based retrievals with model-based analysis to improve quantitative precipitation estimation, warnings, and forecasts.