Articles tagged with Algorithms
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.
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.
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.
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.
Researchers at Stanford University have created a deep learning algorithm that can accurately predict the toxicity of chemicals and associate drugs with side effects using just six data points. This breakthrough could help chemists choose promising candidates and accelerate drug development.
Researchers at Silicon Studio developed an algorithm that uses 'ensemble' and survival analysis methods to predict when users will leave a mobile game. The model can automatically adapt to different games and data, enabling companies to understand user needs and design more entertaining games.
Physicists have developed a new feedback controller to control fusion plasma energy and rotation. The algorithm uses sensors, algorithms, and actuators to modify the plasma's rotation profile and stored energy.
A new algorithm allows users from different network providers to pair up and make better use of the available wireless spectrum, reducing inefficiency in wireless technology. The 'blind' matching algorithm uses a simple learning process and converges to a stable-matching state, enabling mutually beneficial partnerships.
Researchers developed an algorithm that uses Deep Learning to predict the decision of hematopoietic stem cells to become a certain cell type. This enables earlier detection and analysis of blood cell development, paving the way for new treatments and insights into developmental traits.
Researchers discovered that an algorithm called additive increase, multiplicative decrease (AIMD) is used both in engineered systems like the Internet and biological networks like the human brain. This finding sheds light on how the brain manages information and potentially helps understand learning disabilities.
Scientists at the University of Luxembourg developed Equihash, a memory-hard problem algorithm that resolves Bitcoin's centralization issue. This allows for more democratic digital currencies like Zcash, where users can contribute to mining with standard hardware, reducing investment costs and increasing decentralization.
Researchers tested automated approaches to analyze large linguistic datasets, finding that some methods detected cognates correctly with high accuracy. The study suggests a promising future for combining algorithms and expert knowledge to uncover human prehistory and cultural evolution.
MIT researchers incorporated human strategies into automatic planners, achieving significant improvements in performance. By encoding high-level strategies from skilled human planners, they improved the performance of competition-winning algorithms on complex planning problems.
Researchers developed machine learning algorithms to reconstruct 3D protein structures using microscopic images, enabling faster discovery of new drugs for diseases like Alzheimer's and cancer. The approach eliminates prior knowledge requirements, making it possible to study previously inaccessible proteins.
Researchers at Lawrence Berkeley National Laboratory have developed a machine learning algorithm to predict point defects in intermetallic compounds with high accuracy. This method accelerates research on new advanced alloys and lightweight materials for various industries.
Researchers at MIT's Sloan Neuroeconomics Lab have developed a new method to extract correct answers from large groups of people, reducing errors in crowd wisdom surveys. The 'surprisingly popular' algorithm uses the variation between individual responses and predicted popular opinion to identify the correct answer.
A Stanford-developed deep learning algorithm accurately diagnoses skin lesions as well as board-certified dermatologists, with potential to provide life-saving smartphone diagnosis. The algorithm was trained on a database of nearly 130,000 images and tested against 21 dermatologists in identifying malignant carcinomas and melanomas.
Researchers at Cornell University developed an artificial Ms. Pac-Man player that achieved a laboratory score of 43,720, surpassing the existing high score for computerized play. The player uses a decision-tree approach and demonstrates accuracy in predicting ghost movements with 94.6-percent accuracy.
Researchers found that attackers can crack Android Pattern Lock within 5 attempts using video and computer vision algorithm software. Complex patterns are actually easier to crack than simple ones, making shorter, simpler patterns a more secure option.
Researchers develop a treatment algorithm for bionic hand reconstruction in patients with severe nerve damage, offering improved hand function and reduced chronic pain. The study's five patients show significant improvements in hand function, including increased sensitivity and motor control.
Researchers developed a novel-machine learning method to distinguish people with common names, focusing on relational features, text features, and venue features. The new approach is an improvement over existing methods, enabling the identification of previously unencountered individuals.
Researchers created tools called disturbance observers using fractional calculus, which estimate and eliminate disturbances in systems. The new approach outperformed existing methods, particularly when combined to handle highly fluctuating disturbance signals.
Researchers developed an algorithm that extracts diverse subsets from large data sets, improving machine learning efficiency and diversity. The new method is 1 billion times faster than existing algorithms, enabling real-time analysis of vast amounts of data.
Researchers have combined CRISPR gene editing with single-cell genomic profiling to understand nuanced cellular processes. The new technology enables precise manipulation of genes in individual cells, revealing previously unknown functions and advancing the field of genetic engineering.
The UCLA team implanted a spinal stimulator in a California man who broke his neck in a dirt-biking accident, showing early promise in returning hand strength and movement. The device bypasses the injury by training the spinal cord to find alternate pathways, allowing patients to regain mobility in their hands.
The Jefferson Lab-NVIDIA collaboration uses Titan's supercomputer to simulate QCD interactions, achieving speedups of seven- to tenfold for calculations. This enables researchers to explore exotic mesons and advance QCD theory.
Game theory and algorithms improve security by randomizing patrols to evade attackers' predictions. Real-world applications have led to measurable improvements in security for US Coast Guard ferry protection since 2011.
A new recommendation algorithm allows individuals and items to belong to multiple overlapping groups, making it more realistic than existing models. The algorithm's predicted ratings proved more accurate than those from existing systems on five large datasets.
Researchers developed a machine-learning model that can distinguish between pathological hypertrophic cardiomyopathy (HCM) and physiological changes in athletes' hearts, enabling easier diagnoses. The model demonstrated superior diagnostic ability comparable to conventional 2D echocardiographic and Doppler-derived parameters.
Researchers found a universal principle underlying human intelligence, enabling complex brain computations and generalization of knowledge. The Theory of Connectivity proposes a simple mathematical logic guiding neural connections and communications.
Researchers have developed an algorithm to detect fake reviews on ecommerce sites, analyzing behavior and content features to identify deceptive posters. The method outperforms earlier detection algorithms, providing a more accurate picture of product ratings.
Researchers have developed a new algorithm that can efficiently fit probability distributions to high-dimensional data, even when the dataset contains corrupted entries. The algorithm relies on two insights: selecting an appropriate metric for measuring distance from distributions and identifying regions where cross-sections should begin.
Researchers at Northeastern University have proposed a way to optimize power exchange between the main grid and multiple microgrids using consensus-based algorithms. These algorithms allow decentralized generators to communicate with each other and with the main grid, ensuring reliable and cost-effective energy distribution.
The UnBias project aims to establish a system of auditability and build trust in the internet by studying user experience and algorithm design. Researchers will also produce educational materials to support youth understanding about online environments.
Researchers at Sandia National Laboratories explore neural computing applications, including adaptive learning, dynamical systems, and spiking network algorithms. These approaches aim to overcome the static learning bottleneck and enable precise computations.
Researchers construct networks of organelle functional modules in Arabidopsis using a soft thresholding approach. The algorithm identifies strongly co-expressed genes and links them to infer the function of unknown genes.