Articles tagged with Algorithms
Two new predictive algorithms use health data and blood tests to identify high-risk patients, offering improved accuracy in diagnosing cancers. The models identified additional medical conditions associated with increased cancer risk and new symptoms indicative of multiple cancer types.
A USC-led study shows that a quantum annealer outperforms classical algorithms in finding near-optimal solutions to complex problems. The researchers used a D-Wave Advantage processor and implemented error suppression techniques to overcome noise limitations.
A research team from the University of South China has developed a novel algorithm to optimize radiation-shielding design in nuclear reactors. The algorithm, based on a reference-point-selection strategy, efficiently solves many-objective optimization problems and provides optimized shielding solutions for new types of reactors.
The framework utilizes consortium blockchain architecture for immutable records, minimizing data falsification and cyberattacks. AI-driven tools ensure objective data collection and analysis, reducing human bias.
Researchers developed an automated machine learning program to identify potential cardiovascular incidents and fall/fracture risks based on bone density scans. The algorithm shortened screening time and found moderate to high AAC levels in 58% of older individuals, placing them at high risk of heart attack and stroke.
A new robotic system uses cues in a scene to determine a human's objective and quickly identify relevant objects, enabling intuitive assistance in household, workplace, and warehouse settings. The approach achieved 90% accuracy in predicting human objectives and 96% accuracy in identifying relevant objects.
MIT researchers have created a unifying framework that combines existing ideas to improve AI models or create new ones. The 'periodic table of machine learning' categorizes classical algorithms based on the approximate relationships they learn, allowing for fusion of strategies and discovery of new algorithms.
Researchers developed an AI algorithm based on the medical history of over half a million patients, enabling GPs to identify increased risk of lung cancer up to 4 months before diagnosis. This method may also offer early detection for other types of cancer and improved patient outcomes.
MSU is developing a computer program called MOSAIC to create holistic biological profiles from skeletal remains, making the investigative process clearer and more efficient. The project aims to leverage relationships between various structures to provide estimates without biasing results by focusing on individual components.
MIT researchers have developed a new data-driven method that eliminates redundant computations in complex logistical problems. The approach uses machine learning to predict which operations should be recomputed and reduces the solve time for problems like scheduling trains, hospital staff, and factory tasks.
Researchers developed a new framework, PAC Privacy, to maintain AI model accuracy and ensure sensitive data remains safe from attackers. The new variant of PAC Privacy estimates anisotropic noise, reducing computational cost and boosting accuracy.
Researchers at Concordia University have developed a new approach to identifying fake news on social media using the SmoothDetector model. The model integrates probabilistic algorithms with deep neural networks to capture uncertainties and patterns in multimodal data, providing more nuanced judgments of authenticity.
BEAMoCap simplifies 3D animation by eliminating marker suits using AI and machine vision. This reduces production timeline and increases creative flexibility for game developers and film animators.
A new deep learning-based workflow automatically detects and picks teleseismic phases with high efficiency and accuracy. This approach enables seismologists to extract more meaningful data and better understand the physics and dynamics deep inside the Earth.
Researchers warn of misunderstandings in handling AI models, highlighting conditions for confidence in predictions. Explainability methods are crucial to understand algorithmic decisions, but interpreting results requires caution due to AI limitations.
Researchers developed a machine learning-powered fluid simulation model that significantly reduces computation time without compromising accuracy. The new surrogate model maintains the same level of accuracy as traditional particle-based simulations while reducing computation time from approximately 45 minutes to just three minutes.
A new framework developed by MIT researchers allows large language models (LLMs) to break down complex planning problems into manageable parts and find optimal solutions using software optimization tools. The framework achieves an 85% success rate on nine complex challenges, outperforming the best baseline.
Researchers developed an algorithm to calculate biological heart age from ECG data, identifying those at higher risk of cardiovascular events. The study found a strong association between increased biological heart age and increased mortality and cardiovascular outcomes.
Researchers at Kumamoto University have developed a new mathematical modeling technique for linear periodically time-varying systems, enhancing the accuracy of control system models. This breakthrough has profound implications for industries relying on complex control systems, such as autonomous vehicles and aerospace applications, imp...
Researchers developed a new method to include uncertainty in predictive algorithms, ensuring accurate and reliable solutions. The approach uses Markov models to explicitly include uncertainty in specific parameters, allowing for faster predictions and more complete analysis.
Torsten Hoefler's groundbreaking work on high-performance computing and AI has revolutionized the capabilities of supercomputers. His innovations include MPI-3 nonblocking collective operations, 3D parallelism, and routing protocols that power modern AI systems.
A University of Cincinnati study found that machine learning models can aid clinicians in treating patients with spreading depolarizations (SDs), a condition that can cause significant brain damage. The algorithm was able to identify SD events with high sensitivity and specificity, detecting many events not identified by human scoring.
A team of researchers developed an innovative acoustic method to detect hidden stones in coffee beans, preventing damage to grinding machines. The system uses empirical mode decomposition and field programmable gate arrays to identify stone presence with near-perfect accuracy.
A new study in JSTAT introduces a hiring strategy model that suggests dividing candidates into two groups: those to be evaluated and rejected upfront, and those to be selected based on their performance relative to previous hires. The optimal approach depends on the company's objective, balancing quality and speed.
A new study by Carnegie Mellon University researchers found that real-time AI feedback increases perceived trustworthiness and enhances workers' sense of their own work quality. This leads to increased trust in AI-generated performance ratings, particularly in non-routine work settings.
Barto and Sutton introduced the main ideas, constructed mathematical foundations, and developed algorithms for reinforcement learning, a key approach for intelligent systems. Their work has been influential in AI research, with applications in areas like robotics, network optimization, and natural language processing.
Andrew Barto and Richard Sutton's pioneering work in reinforcement learning has been recognized with the 2024 ACM A.M.Turing Award. Their algorithmic foundations have led to significant advances in AI, including deep reinforcement learning.
A new AI prediction model, UNAFIED, uses machine learning to predict whether a patient has or might develop detectable AFib within two years. The non-invasive approach provides a practical option for proactive screening of patients at elevated risk for AFib.
Researchers developed a new method to search through billions of molecules to identify potential anti-inflammatory drug candidates. The method uses computer algorithms to explore vast chemical space and has the potential to speed up the costly drug development process.
The FDA has approved a new treatment for Parkinson's disease that can adjust to the individual's brain activity, providing precise stimulation. This technology, known as adaptive deep brain stimulation (aDBS), detects patterns of brain activity and delivers tailored electric pulses to reduce symptoms.
Researchers propose a new local electricity market to harness the power of homeowners' grid-edge devices in case of outages or attacks. Devices like solar panels and electric vehicles can pump power into the grid or rebalance consumption.
Researchers evaluated three SIF retrieval algorithms to improve diurnal patterns of vegetation photosynthesis. The Band Shape Fitting (BSF) algorithm demonstrated superior performance in capturing diurnal variations, correlating with R² 0.85 and accurately monitoring vegetation ecosystems.
A recent study has identified that the neuronal subtype responds best to immunotherapy, while other subtypes exhibit lower response rates. The researchers developed a machine-learning algorithm using large public data sets to predict treatment response based on tumor mutational burden and immune cell infiltration.
Researchers at North Carolina State University have developed a new technique to improve the efficiency of food-delivery operations. The technique accounts for key factors such as food supply, location, and vehicle capacity to determine optimized routes that make food delivery faster and more fuel-efficient.
A new study at the University of Gothenburg found that a software robot can detect side effects faster than physicians during amiodarone treatment for cardiac arrhythmia. The robot also recommends appropriate intervals between lab tests, aligning with standard practices and reducing unnecessary testing.
A new study from the University of South Australia found that most people trust AI in situations where the stakes are low, such as music suggestions. However, those with poor statistical literacy or little familiarity with AI were just as likely to trust algorithms for trivial choices as they were for critical decisions. The study also...
The Association for Computing Machinery (ACM) has named 56 professionals as Distinguished Members, selected by peers for significant technical achievements and volunteer service. The program recognizes up to 10% of the global ACM membership based on professional experience and impact in computing.
Lydia Kavraki was elected to the National Academy of Engineering for her groundbreaking contributions to robotics, biomedicine, and artificial intelligence. Her work has revolutionized motion-planning algorithms and enabled robots to collaborate with humans safely.
A novel scheduling model abstracts the entire sortie process into a hybrid flow-shop scheduling problem, taking into account task priorities and resource constraints. The solution framework was validated in a simulation environment and shows significant improvement in sortie efficiency compared to existing strategies.
Researchers at Hiroshima University have discovered complex interactions between Pseudo-nitzschia groups and other algal species, suggesting salinity has a more significant influence than previously thought. This understanding is crucial for predicting harmful algal blooms, which cause substantial damage to the aquaculture industry.
Hannah Dailey, an associate professor of mechanical engineering at Lehigh University, has been recognized with the Presidential Early Career Award (PECASE) for her innovative research on fracture healing. Her virtual mechanical test can identify nonunions early in the healing process, allowing for earlier surgical intervention.
Researchers created an automated system that enables developers to build algorithms taking advantage of two types of data redundancy, boosting computation speed and reducing energy consumption. The system can optimize machine-learning algorithms for a wide range of applications, including scientific computing.
Researchers at Lancaster University have developed a new method to detect Alzheimer's disease by analyzing changes in brain oxygenation dynamics and neuronal function. The study found that individuals with Alzheimer's disease exhibit altered respiratory frequency, which may be an early indicator of the condition.
The TU Graz AI system optimises vehicle components using simulation models and evolutionary algorithms to reduce development time by several months. It considers multiple objectives including production costs, efficiency and package space requirements, as well as CO2 emissions across the entire supply chain.
Researchers found that training AI agents in a less noisy environment can lead to better performance than traditional methods. The indoor training effect suggests that constructing simulated environments with specific noise levels can improve AI learning.
A new algorithm proposes measuring quadriceps muscle mass for more accurate sarcopenia diagnosis, potentially leading to earlier detection and better treatment options. Ultrasound imaging is recommended as a cost-effective and practical solution for diagnosing sarcopenia in clinical settings.
A new large language model, 'llm-jp-3-172b-instruct3', has been released with approximately 172 billion parameters, trained from scratch using 2.1 trillion tokens of data. This model surpasses GPT-3.5 performance on benchmarks such as 'llm-jp-eval' and 'llm-leaderboard'.
The Association for Computing Machinery (ACM) has named 55 Fellows for their transformative contributions to computing science and technology. The inductees represent a diverse range of fields, including cybersecurity, artificial intelligence, human-computer interaction, machine learning, and programming languages.
Anil Jain and Michael I. Jordan received the BBVA Foundation Award in Information and Communication Technologies for their pioneering work on machine learning, enabling transformative technologies like biometrics and artificial intelligence. Their research has unlocked applications of far-reaching impact on society.
A new 6D pose dataset has been introduced, providing high-quality RGB and depth images with annotated 6D pose data. This dataset achieves state-of-the-art accuracy rates of 97.05% and 98.09% for robotic grasping and automation applications.
Researchers from Southern Methodist University and University of Michigan found A-B testing limitations that affect user response to ads, leading to misinterpreted data. The study highlights the importance of recognizing these limitations to make informed marketing decisions.
Researchers from Singapore, Japan and US developed an advanced swarm navigation algorithm for cyborg insects that prevents them from getting stuck in challenging terrain. The new algorithm represents a significant advance in swarm robotics and could pave the way for applications in disaster relief and search-and-rescue missions.
Researchers at MIT have developed Boltz-1, an open-source AI model that achieves state-of-the-art performance in predicting biomolecular structures. The model surpasses AlphaFold3, which is limited to academic research and commercial use, by incorporating new algorithms and improving prediction efficiency.
The authors introduce advanced methodologies for integrating maintenance strategies and structural health monitoring to extend infrastructure service life. Topics include data-driven decision-making, multi-objective optimization, cost-benefit analysis, and the role of data analytics in managing uncertainties.
A study of 9,000 US-based participants reveals that they prioritize efficiency over fairness when choosing between human and algorithmic decision-makers. Notably, Republicans prefer humans more than Democrats, yet most claim fairness is a top priority.
Physicists at the University of Michigan have developed an algorithm that enables materials to learn and adapt, mimicking brain-like behaviors. This breakthrough has implications for the development of advanced materials with self-tuning properties.
A new monitoring system uses 'virtual sensing' to accurately estimate CO₂ and NOx emissions based on driving behavior. The system provides personalized environmental impact assessments, empowering drivers to manage their emissions.
A team of Caltech researchers has developed an algorithm called Spectral Expansion Tree Search (SETS) that enables autonomous robots to determine the best movements to make as they navigate the real world. SETS uses control theory and linear algebra to find natural motions that use a robotic platform's capabilities to its fullest extent.
Researchers develop a simple fix to an existing technique, enabling the generation of sharp, high-quality 3D shapes that rival top model-generated 2D images. The new approach improves upon previous methods by avoiding costly retraining and complex postprocessing.
Researchers from the University of South Australia have developed a celestial navigation system that uses visual data from stars to provide an alternative means of nighttime navigation in environments where GPS is unavailable or unreliable. The system has been tested on a fixed-wing drone and demonstrated accurate positioning within fo...