Articles tagged with Algorithms
Researchers employed a computer algorithm to analyze nearly 15,000 landscape paintings, finding that compositional structures evolved systematically over time. The study suggests a potential bias in art curators' and historians' selections, contradicting the prevailing view of diverse artistic expression.
A new algorithm helps track fast charged particles in plasma, which could influence fusion reactions. The algorithm conserves energy during pitch-angle scattering, a critical process in fusion plasma.
A new machine learning algorithm has been developed to isolate dynamic neural patterns in brain signals that relate to specific behaviors, such as finger movements. The algorithm resolves the challenge of isolating these patterns, which can be masked by other activities, and enhances the decoding of behaviors from brain signals.
A new open-source tool, CRYLOGGER, analyzes thousands of Android apps to detect cryptographic misuses without requiring access to the app's code. The study found that nearly all popular Android apps contain code or use libraries that do not strictly adhere to security standards.
Researchers from the University of Michigan found that the vast majority of field massive stars in the Small Magellanic Cloud are 'runaways,' or stars ejected from clusters. They discovered that these stars could have formed in isolation or were dynamically ejected due to unstable orbital configurations.
Purdue University is leading a $3.7 million research project to create more secure machine learning algorithms for autonomous systems. The goal is to develop a robust, distributed and usable software suite to prevent AI hacking and ensure the accuracy of autonomous machines on the battlefield.
A new algorithm could help reduce stress on health systems in the UK and beyond by redistributing ICU patients across hospitals. The load balancing method, developed by Queen Mary University of London, has been shown to enable access for up to 1000 additional cases in the UK.
A team of researchers from Rensselaer Polytechnic Institute developed a deep neural network that can perform nearly as well as more complex dual-energy CT imaging technology. The algorithm produced high-quality approximations with a relative error of less than 2% using single-spectrum CT data.
Researchers found that AI techniques are being used to process information about brain structure and connectivity, assess surgical candidacy, and predict disease trajectory. However, they emphasize the need for explainable algorithms to ensure trust in algorithmic prescriptions or diagnoses.
Researchers created an abstract language that describes protein molecules' shapes and structures, enabling predictions of their dynamics. This method uses machine learning algorithms to analyze molecular movements and provides insights into disease causes and targeted drug therapies.
Researchers developed an algorithm to predict sex-specific adverse drug effects, identifying 20,817 adverse drug effects posing sex-specific risks. The algorithm addresses confounding biases and provides an opportunity to minimize adverse events by tailoring drug prescription and dosage to sex.
Skoltech researchers propose a fast and accurate numerical method to address the low spatial resolution of EEG studies. The new approach directly backpropagates measured signals from the skin down to the cortex, increasing accuracy for source localization while speeding up processing.
Cyber-physical systems face challenges due to large volumes of data flowing through communication networks, causing routing and queuing delays that degrade system quality. The new algorithms developed by researchers strike a balance between communication sparsity, delay, and performance, ensuring safe and stable operation.
The University of Texas at Austin has been selected to lead the NSF AI Institute for Foundations of Machine Learning, aiming to develop new classes of algorithms for more sophisticated AI technologies. The institute will focus on addressing challenges such as noise and diffuseness in modern datasets.
A new machine learning algorithm confirms 50 new exoplanets, ranging from Neptunes to Earth-sized worlds, by distinguishing between real and false positives. The technique outperforms previous methods, can be automated, and improves with further training.
The method can be used in a portable, tabletop device to rapidly identify known and emerging opioid fentanyl substances, aiding in the safety of law enforcement and military personnel. The AI algorithm had a 92.5 percent accuracy rate for correctly identifying molecules related to fentanyl.
Researchers have developed an efficient method to estimate camera movement, reducing the number of hypotheses generated from up to five to one. The new approach allows for real-time execution of pose estimation, with a complete algorithm taking only 29 milliseconds per frame.
Scientists at NIST have found a way to significantly enhance the accuracy of key information on how heat affects the stability of folded DNA structures. The novel mathematical algorithm automatically accounts for unknown effects, allowing scientists to design durable and complex structures made from DNA.
A new tool, FairCo, was developed to improve the fairness of online rankings by giving equally relevant choices roughly equal exposure and avoiding preferential treatment for high-ranked items. This can correct unfairness in existing algorithms and curtail personal choice.
Experts have developed a platform for self-testing AI medical services, allowing for automated validation and improvement. The platform provides an opportunity to fine-tune algorithms with unlimited access to data instances, minimizing human factor manipulation.
A new calibration-free version of a physical-based method can better detect long-term trends in continental-scale land evaporation rates than recent remote sensing-based approaches. This improvement is crucial for upgrading general circulation models' existing evaporation estimation algorithms to produce better climatic predictions.
The 2020 Joint Statistical Meetings feature cutting-edge research on COVID-19 modeling, including nowcasting and forecasting. Researchers also presented advancements in precision medicine for stem cell transplants, improving patient outcomes by extending lives.
Researchers Xifeng Yan and Yu-Xiang Wang developed a deep learning tool to forecast COVID-19 trends by community. The model, called Transformer, uses attention mechanisms and U.S. Census data to provide hyper-local forecasts with high accuracy.
The EBRAINS human brain atlas is a comprehensive digital map of the cellular architecture, showcasing 250 structurally distinct areas based on analysis of 10 brains. This atlas enables researchers to better understand brain functions and mechanisms of diseases.
Researchers at Cornell University have identified a problem that holds the key to whether all encryption can be broken. The study connects two areas of mathematics - cryptography and algorithmic information theory - to show that a natural computational problem characterizes the feasibility of basic cryptography.
Researchers at LMU Munich used smartphone data to analyze users' personalities, finding strong correlations between behavior patterns and self-assessed traits. The study focused on the Big Five personality dimensions, revealing that certain types of digital behavior are more informative for specific self-assessments.
Researchers at Ruhr-University Bochum developed a new method for efficiently identifying deep-fake images by analyzing objects in the frequency domain. The study found that images generated by GANs exhibit artefacts in the high-frequency range, which can be used to distinguish them from real photos.
Researchers develop new algorithm to measure crystal growth rate in supercooled liquids, achieving accuracy orders of magnitude higher than existing methods. The approach is based on molecular dynamics simulation and can be applied to various systems with different physicochemical properties.
Researchers at Cornell University used AI to investigate how reflection changes images, discovering clues like facial features and beards that can differentiate originals from reflections. The study has implications for training machine learning models and detecting faked images.
Researchers at Carnegie Mellon University developed an efficient new way to quickly analyze complex geometric models by using Monte Carlo methods. This approach eliminates the need to divide shapes into meshes, reducing errors and increasing computation speed.
Researchers used recommender algorithms to suggest antiviral compounds based on latent relationships in chemical and biological data. They successfully pinpointed promising drug candidates with anti-SARS-CoV-2 activity.
Researchers at University of Cambridge developed a context-aware AI system that reduces the 'communication gap' by up to 96% for nonverbal people with motor disabilities, enabling faster conversation rates.
Researchers developed a framework using graph theory to optimize digital communication networks, finding cliques within them for efficient data transmission and improvement in throughput by up to 30%. This approach has far-reaching implications for future networks, including the internet of things with larger volumes of data.
Researchers at Oregon Health & Science University have developed an AI algorithm that uses data from continuous glucose monitors and wireless insulin pens to provide guidance on adjustments. The algorithm, paired with a smartphone app called DailyDose, generated recommendations in agreement with physicians 67.9% of the time.
A new study urges judges to scrutinize algorithmic risk assessment tools used in criminal cases due to biases and weak statistical evidence. The review recommends three steps: fitness, accuracy, and reliability assessments to ensure the tool's relevance, performance, and trustworthiness.
A team of engineers at the University of Cambridge has successfully trained a robot chef to prepare an omelette using machine learning, with improved taste and texture. The robot's culinary skills were refined to produce a reliable dish that meets human standards, overcoming the subjective nature of cooking.
The Fast Loaded Dice Roller (FLDR) algorithm simulates the roll of loaded dice to produce random integers with the best combination of speed, accuracy, and low memory requirements. FLDR can use up to 10,000 times less memory storage space than existing methods.
Researchers found that worker autonomy and transparency are crucial for crowdwork environment, while algorithmic management can enhance productivity when framed as promoting autonomy. The study suggests ways to improve algorithmic management via AI assistance.
Researchers developed an algorithm to predict coastal currents using dynamical systems theory and ocean data. This new tool promises to enhance search and rescue techniques at sea, potentially saving lives.
A new algorithm developed by researchers at the University of Córdoba improves response capacity to forest fires. The tool predicts fire fighting difficulty and offers an extinction difficulty index, helping authorities prioritize management tasks and optimize resources.
A new algorithm can continuously monitor a person's sympathetic nervous system activation using skin conductance and heart rate measurements, aiding in the development of wearable monitors for anxiety disorders.
A new study suggests that machine learning and AI in medical image reconstruction algorithms can result in myriad artefacts and major errors, leading to false positives and false negatives. The effects were typically not present in non-AI based imaging techniques.
Australian and US researchers used advanced computational methods to detect different structures of RNA, which until now could not be distinguished. The team discovered that changes in RNA structure influenced how the virus behaved, including which proteins it produced.
A recent study by Penn State researchers found that providing users with the option to adjust privacy and content settings on Amazon Alexa does not necessarily increase their trust in the platform. In fact, for power users, it can have an unfavorable effect. However, customizing content increased trust among sensitive users.
Fugu outperforms BBA in terms of least interruption time, highest image resolution and consistency of video quality, keeping viewers engaged for 5-9% longer
Researchers propose a new framework to quantify the predictability of temporal networks, which encodes the ordering and causality of interactions between nodes. The study found that the contributions of topology and temporality to network predictability vary significantly across different types of real networks.
A new startup, Oqullo, is developing a platform that utilizes computer visual algorithms and network cameras to gather demographic information about customers. This data can be used to offer personalized experiences in shopping, traveling, and other consumer activities.
Researchers used a computer algorithm inspired by slime mould to create a three-dimensional map of the cosmic web structure in the local Universe. The analysis revealed that intergalactic gas is organised into filaments and detected at distances over 10 million light-years from galaxies.
Researchers at Ruhr-University Bochum used artificial intelligence to predict the structure of thin films, reducing the need for extensive experiments. The team developed a generative model that can generate images of the surface of a layer under specific process parameters, enabling the identification of optimal material formulas.
Iowa State University engineers develop a closed-form solution for the inverse chirp z-transform, enabling efficient computation on the unit circle and lifting restrictions of existing algorithms. The algorithm achieves high accuracy with 64-bit floating-point numbers and operates in O(n log n) time.
Researchers at six universities, including UT Austin, are launching a partnership to turn AI 'black boxes' into human-understandable computer code. This will enable solving hitherto unsolvable problems in fields like drug design and cognitive science.
A new computer algorithm inspired by the mammalian olfactory system rapidly learns patterns and identifies smells even with strong sensory interference. The algorithm is applied to a neuromorphic computer chip, Loihi, which can learn to identify patterns or perform tasks a thousand times faster than traditional methods.
Researchers have successfully mapped the cosmic web's filamentary structure using a slime mold-inspired algorithm, providing insights into dark matter's role in shaping the universe. The study revealed that denser regions of intergalactic gas are organized into filaments that stretch over 10 million light-years from galaxies.
By using approximations instead of explicit kernels, researchers have accelerated machine learning speeds and improved AI's ability to handle large datasets. The new approach uses statistics to derive a nearly accurate kernel that can be computed much faster than traditional methods.
Researchers have developed an algorithm that uses the transit time of sound waves to assign echoes to specific walls. The drone's six degrees of freedom are sufficient for optimal microphone placement, reducing ghost wall detection. This innovation opens a new pathway towards practical applications in various fields.
Researchers from UNSW Sydney have successfully analyzed the complex structure of benzene in 126 dimensions, shedding light on its stability and interactions. The discovery reveals unexpected electron behavior, where up-spin double-bonded electrons interact with down-spin single-bonded electrons.
A new algorithm has been discovered that can accurately register two point sets consisting of millions of points in a fraction of the time taken by conventional methods. The algorithm's accuracy is not compromised despite using approximations, and it has wide-ranging applications in computer graphics and computer vision.
Researchers developed a scalable method for estimating crop productivity in real time using satellite data, in-field camera networks, and ground measurements. The method provides highly accurate estimates of leaf area index (LAI) and can be used to detect underperforming fields or segments that need targeted management practices.
Using Euclid's algorithm as an example, Dr. Lamport showed how a precise mathematical formula can be used to specify an algorithm, making it more efficient and easier to debug. By using math instead of code, developers can reduce the size of their programs by up to ten times and make debugging easier.
Computational biologists at Carnegie Mellon University have developed a new algorithm, MOCHI, to identify communities within the cell nucleus. The algorithm uses spatial arrangement and genetic interactions to subdivide interwoven nuclear components into groups.