Articles tagged with Algorithms
Comprehensive exploration of living organisms, biological systems, and life processes across all scales from molecules to ecosystems. Encompasses cutting-edge research in biology, genetics, molecular biology, ecology, biochemistry, microbiology, botany, zoology, evolutionary biology, genomics, and biotechnology. Investigates cellular mechanisms, organism development, genetic inheritance, biodiversity conservation, metabolic processes, protein synthesis, DNA sequencing, CRISPR gene editing, stem cell research, and the fundamental principles governing all forms of life on Earth.
Comprehensive medical research, clinical studies, and healthcare sciences focused on disease prevention, diagnosis, and treatment. Encompasses clinical medicine, public health, pharmacology, epidemiology, medical specialties, disease mechanisms, therapeutic interventions, healthcare innovation, precision medicine, telemedicine, medical devices, drug development, clinical trials, patient care, mental health, nutrition science, health policy, and the application of medical science to improve human health, wellbeing, and quality of life across diverse populations.
Comprehensive investigation of human society, behavior, relationships, and social structures through systematic research and analysis. Encompasses psychology, sociology, anthropology, economics, political science, linguistics, education, demography, communications, and social research methodologies. Examines human cognition, social interactions, cultural phenomena, economic systems, political institutions, language and communication, educational processes, population dynamics, and the complex social, cultural, economic, and political forces shaping human societies, communities, and civilizations throughout history and across the contemporary world.
Fundamental study of the non-living natural world, matter, energy, and physical phenomena governing the universe. Encompasses physics, chemistry, earth sciences, atmospheric sciences, oceanography, materials science, and the investigation of physical laws, chemical reactions, geological processes, climate systems, and planetary dynamics. Explores everything from subatomic particles and quantum mechanics to planetary systems and cosmic phenomena, including energy transformations, molecular interactions, elemental properties, weather patterns, tectonic activity, and the fundamental forces and principles underlying the physical nature of reality.
Practical application of scientific knowledge and engineering principles to solve real-world problems and develop innovative technologies. Encompasses all engineering disciplines, technology development, computer science, artificial intelligence, environmental sciences, agriculture, materials applications, energy systems, and industrial innovation. Bridges theoretical research with tangible solutions for infrastructure, manufacturing, computing, communications, transportation, construction, sustainable development, and emerging technologies that advance human capabilities, improve quality of life, and address societal challenges through scientific innovation and technological progress.
Study of the practice, culture, infrastructure, and social dimensions of science itself. Addresses how science is conducted, organized, communicated, and integrated into society. Encompasses research funding mechanisms, scientific publishing systems, peer review processes, academic ethics, science policy, research institutions, scientific collaboration networks, science education, career development, research programs, scientific methods, science communication, and the sociology of scientific discovery. Examines the human, institutional, and cultural aspects of scientific enterprise, knowledge production, and the translation of research into societal benefit.
Comprehensive study of the universe beyond Earth, encompassing celestial objects, cosmic phenomena, and space exploration. Includes astronomy, astrophysics, planetary science, cosmology, space physics, astrobiology, and space technology. Investigates stars, galaxies, planets, moons, asteroids, comets, black holes, nebulae, exoplanets, dark matter, dark energy, cosmic microwave background, stellar evolution, planetary formation, space weather, solar system dynamics, the search for extraterrestrial life, and humanity's efforts to explore, understand, and unlock the mysteries of the cosmos through observation, theory, and space missions.
Comprehensive examination of tools, techniques, methodologies, and approaches used across scientific disciplines to conduct research, collect data, and analyze results. Encompasses experimental procedures, analytical methods, measurement techniques, instrumentation, imaging technologies, spectroscopic methods, laboratory protocols, observational studies, statistical analysis, computational methods, data visualization, quality control, and methodological innovations. Addresses the practical techniques and theoretical frameworks enabling scientists to investigate phenomena, test hypotheses, gather evidence, ensure reproducibility, and generate reliable knowledge through systematic, rigorous investigation across all areas of scientific inquiry.
Study of abstract structures, patterns, quantities, relationships, and logical reasoning through pure and applied mathematical disciplines. Encompasses algebra, calculus, geometry, topology, number theory, analysis, discrete mathematics, mathematical logic, set theory, probability, statistics, and computational mathematics. Investigates mathematical structures, theorems, proofs, algorithms, functions, equations, and the rigorous logical frameworks underlying quantitative reasoning. Provides the foundational language and tools for all scientific fields, enabling precise description of natural phenomena, modeling of complex systems, and the development of technologies across physics, engineering, computer science, economics, and all quantitative sciences.
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.
A team of Chan Zuckerberg Biohub scientists developed a deep-learning method, dubbed
A University of Washington team created a new tool that can design a 3D-printable passive gripper and calculate the best path to pick up an object. The designed grippers and paths were successful for 20 out of 22 objects tested, with two challenging shapes being the wedge and pyramid shape.
A new AI program identified four variables for a swinging double-pendulum, but the remaining two variables remain a mystery. The AI successfully predicted physical phenomena in other systems, such as air dancers and lava lamps, with varying numbers of variables.
A new AI algorithm, IcePic, has been developed to predict ice crystal formation with high accuracy. It outperformed human scientists in an online quiz, identifying areas where humans were wrong and providing valuable insights for atmospheric science research.
A new method can improve explosion detection by training computers to recognize synthetic infrasound signals, which reflect regional and global atmospheric changes. This approach broadens the usefulness of single-element infrasound microphones for detecting subtle explosion signals in near real-time.
Researchers at Max Planck Institute for Intelligent Systems created a robot dog named Morti that can walk smoothly within an hour. The robot uses a Bayesian optimization algorithm to learn from sensor data and adapts its virtual spinal cord, allowing it to optimize its walking pattern and minimize stumbling.
A study by Carnegie Mellon University researchers found that algorithmic transparency can have positive effects for firms, allowing them to motivate agents to improve valuable features. However, transparency may not always be beneficial for agents, as it can lead to a loss of predictive power and disadvantage high-type agents.
Researchers created a machine-learning algorithm to predict neighborhood racial segregation, showing 86% accuracy in one test. The map analyzed census data and showed less segregated areas becoming more mixed by 2030.
Researchers used logistic regression and a recommendation algorithm to predict CME arrival times, achieving better results than using either method alone. The hybrid model improved forecast accuracy by providing a reference for similar historical events.
Researchers developed a neural network algorithm that recognizes emotions and engagement from video images of faces, outperforming existing models in accuracy. The system can be integrated into video conferencing tools and online learning systems to analyze participant engagement and emotions.
A team of researchers led by Danilo Vasconcellos Vargas has developed a new method called 'Raw Zero-Shot' to evaluate the robustness of artificial neural networks in image recognition. The study found that Capsule Networks produced the densest clusters, indicating improved transferability and potential solutions for improving AI robust...
Researchers developed an automated method to create 3D images of leaked gas clouds, enabling precise location, volume, and concentration determination. This technology can provide early leak warnings, assess risk, or determine the best way to fix leaks in large facilities with stored toxic chemicals.
A new robotic system, FuseBot, has been developed to efficiently retrieve buried objects in piles. The system uses radio frequency signals and computer vision to reason about the probable location and orientation of objects under the pile, enabling it to find more hidden items than a state-of-the-art robotics system in half the time.
Researchers developed open-source software SHRY to find distinct substitution patterns in disordered systems, reducing computation time. The software uses group theory and canonical augmentation to efficiently analyze crystal structures with random substitutions.
A newly expanded data set of brain scans from stroke patients called ATLAS now includes 1,271 MRI images with manually segmented lesions, facilitating large-scale stroke recovery research. Researchers hope to develop algorithms to automate lesion segmentation, enabling clinicians to predict patient responses to therapies.
A novel algorithm, BLIND, enables robots to navigate through environments with obstacles by incorporating human feedback. Humans provide labels to refine the robot's trajectory, avoiding obstacles efficiently.
Researchers found that people are less morally outraged when gender discrimination occurs due to an algorithm rather than direct human involvement. The study's findings have broader implications for efforts to combat discrimination and may affect how companies are held liable.
SeqScreen, an open-source software toolkit, accurately characterizes short DNA sequences to detect pathogenic sequences. The program uses a curated database of thousands of gene sequences representing 32 types of virulence functions.
A team of scientists has developed a novel computational approach to analyze DNA sequences of thousands of bacteria, revealing previously unknown gene clusters responsible for producing metabolites of interest. The study highlights the potential of these bacterial compounds in treating colon cancer and improving treatment options.
A study by Thorsten Lehnert shows that corporate managers' behavior is linked to investor sentiment, predicting investment strategy success. The researcher found a significant relationship between market-level euphoria and investment factor performance, outperforming static strategies.
Researchers developed an algorithm to improve matching efficiency, considering user preferences and behavior. The new algorithm shows improved results in field experiments, with at least 27% more matches than the current one.
Researchers at MIT identified a flawed analysis of website-fingerprinting attacks and developed new techniques to prevent them. They found that attackers can use machine-learning algorithms to decode signals leaked between software programs, enabling them to obtain private information.
A new training algorithm for deep spiking neural networks (SNNs) uses biologically plausible spatiotemporal adjustment to improve performance and reduce energy consumption. This approach achieves competitive classification accuracy with only 3% of the energy used by traditional artificial neural networks.
A team of researchers from Waseda University developed a novel solution to efficiently solve complex optimization problems using Ising machines. Their hybrid algorithm reduces residual energy and reaches more optimal results in shorter time, increasing the machine's applicability across industries and sustainability practices.
A new AI method can distinguish between the overall sounds of healthy and unhealthy coral reefs, making it a valuable tool for monitoring reef health. The technique uses machine learning to analyze sound recordings and track the progress of reef restoration projects.
Researchers at Cornell University have developed a new algorithm for autonomous underwater sonar imaging that significantly improves speed and accuracy for identifying objects such as explosive mines and sunken ships. The new approach, called informative multi-view planning, integrates information about object locations with sonar proc...
MIT researchers develop an interactive design pipeline enabling users to create customized robotic hands with tactile sensors. The platform streamlines the process, allowing users to adjust palm and fingers and integrate tactile sensors, resulting in complex tasks like picking delicate items or using tools being performed flawlessly.
Researchers at Sainsbury Wellcome Centre found that mice can choose the best escape route after only 10 minutes of exploration, without needing to experience threat. The study suggests that mice use innate heuristics and natural exploration to learn this information.
Researchers used big data and machine learning methods to provide insight into equity valuation over the past two decades. However, a review by Professor Doron Nissim identified crucial areas that require more research attention.
Researchers have successfully processed sequences with a large neural network while consuming significantly less energy on neuromorphic hardware. This breakthrough showcases the potential of neuromorphic technology to improve the energy efficiency of AI workloads.
A machine learning algorithm has outperformed astronomers in analyzing microlensing data to find new exoplanets, revealing connections hidden in complex mathematics from general relativity. The AI algorithm uncovered a degeneracy that had been missed by experts, suggesting a broader theory is likely incomplete.
A new computer-aided diagnostic tool, Deep-Lung Parenchyma-Enhancing (DLPE), uses artificial intelligence to reveal signs of pulmonary fibrosis in COVID long-haulers, helping to explain respiratory symptoms and improve disease management.
A study by Thorsten Lehnert reveals that price noise from trading activities of mutual funds explains the success of 'bets against beta' strategies. These strategies, which involve taking short positions in assets with higher betas and long positions in those with lower betas, result in significant positive returns.
Researchers at MIT developed a technique that enables an autonomous vehicle to plot a provably safe trajectory in highly uncertain situations. The algorithm considers probability of observing different environmental conditions and obstacles, and formulates trajectory planning as a probabilistic optimization problem.
A new distributed learning technique, GD-SEC, reduces communication requirements in wireless architecture, improving efficiency and reducing computational cost. The method employs data compression to transmit only meaningful, usable data, enhancing the impact of machine learning while minimizing its limitations.
A new algorithm forecasts which hospitalized patients are at the highest risk of death from COVID-19, regardless of immune protection status or virus variant. The tool, called COVID-19 Disease Outcome Predictor (CODOP), uses blood measurements to predict prognosis and can help direct critical care resources.
Researchers at Duke University have developed a machine learning algorithm that incorporates known physics into neural networks, allowing for new insights into material properties and more efficient predictions. The approach helps the algorithm attain transparency and accuracy, even with limited training data.
A new observational study from Vanderbilt University Medical Center compares an artificial intelligence algorithm with face-to-face screening for estimating suicide risk. The ensemble learning method produced nearly always better predictions across the board compared to C-SSRS or VSAIL alone.
Researchers at North Carolina State University have developed FAXID, a hardware-based approach for detecting ransomware that is significantly faster than software-based methods. In proof-of-concept testing, FAXID demonstrated accuracy comparable to XGBoost but with speeds up to 65.8 times faster.
A new nanosensor platform uses machine learning to analyze spectral signatures of carbon nanotubes for early detection of ovarian cancer. The approach detects biomarkers and recognizes the cancer itself, offering a promising alternative to traditional methods.
Harbir Antil leads a project to create scalable algorithms for optimizing nonlinear dynamical systems with uncertainty. The research aims to enable automated design, data analysis, and optimization using randomized preconditioning and compression methods.
Researchers create a new method for detecting diagnostic markers of epilepsy using EEG and MEG, enabling precise localisation of epileptogenic cortical structures. The biomimetic algorithm improves the effectiveness of neurosurgical interventions for epilepsy patients with resistant treatment.
Researchers developed an algorithm to identify users' basic needs from their Instagram posts, images, and captions. The study analyzed 86 profiles in Spanish and Persian, achieving promising accuracy and complementary information between visual and textual cues.
Researchers developed an algorithm to quickly identify two cancer drugs that work well together, reducing testing time from three years to eight weeks. The new method uses gene expression data to prioritize potential combinations and has been confirmed in lab tests.
Xie aims to develop algorithms that reflect human domain knowledge and reasoning patterns, increasing trust in machine learning models. He also explores algorithmic fairness, considering multiple perspectives on what's considered fair, to address concerns about biased decision-making.
A University of Auckland research team analyzed the legal documents of Spotify and Tinder to understand their algorithmic recommendations. The study found that both platforms collect and use personal data without transparency, influencing users' choices in various ways.
Researchers at Tokyo University of Science propose two new search strategies to reduce the computational cost of rebalancing in bicycle-sharing systems. The approaches focus on finding feasible solutions more efficiently and redefine the problem to minimize solving time.
Researchers have developed a method using nanomagnets to perform artificial intelligence, slashing energy costs and offering huge efficiency gains. The technology uses 'nanomagnetic states' to process and store data, cutting out the need for software simulation.
A new method reduces computational complexity of traffic models, making them operate more efficiently. The modified algorithm breaks down complex forecasting questions into smaller problems that can be solved in parallel, significantly reducing run time. This approach also allows for a good enough solution within an error bar, rather t...
Scientists have developed a machine learning algorithm that can accurately predict the lifetimes of different battery chemistries using as little as a single cycle of experimental data. The technique could reduce costs and accelerate the development of new battery materials, enabling researchers to quickly evaluate and test multiple ma...
Researchers developed a decision support program to reduce blood draws in pediatric intensive care units (PICU), leading to reduced antibiotic prescriptions without increasing sepsis risk. The program resulted in significant reductions in blood culture rates and unnecessary medication use.
James Anderson, assistant professor of electrical engineering at Columbia University, has received a National Science Foundation (NSF) CAREER award for his work on developing randomized algorithms for the analysis and control of large-scale cyber-physical systems. His goal is to provide robust, reliable, and secure autonomy to large-sc...
Researchers from Dartmouth College used artificial intelligence to draft wine and beer reviews, finding agreement between human and machine-generated reviews. The team also developed a system to write review syntheses, aggregating elements from existing reviews to provide limited but relevant information about products.
Researchers from Bar-Ilan University discovered that brain learning occurs mainly in dendritic trees, where the trunk and branches modify their strength. This finding paves the way for a new type of AI hardware and algorithms with comparable success rates to existing parallel GPUs.
Researchers have developed a novel image reconstruction method using Vision Transformer (ViT) architecture to overcome limitations of conventional methods. The proposed algorithm enables the acquisition of high-quality images in a short computing time, suitable for real-time capture and various applications.
A new algorithm developed by researchers at the University of Córdoba uses Artificial Intelligence to predict crops' water needs with greater accuracy and precision. The algorithm requires fewer meteorological variables than traditional methods, making it a more efficient tool for managing water resources.
Researchers at the University of Georgia developed a model to prioritize land conservation in the Upper Chattahoochee Watershed, which provides drinking water to 95 cities. The model considers ecosystem services like water, carbon, and habitat, and favors higher connectivity among parcels of land.