Articles tagged with Algorithms
A team of researchers uses Quantum Fisher Information to separate individual parameter relevance from cross-parameter correlations in QAOA. This analysis reveals strongly non-uniform parameter relevance that varies with circuit architecture and depth.
Florida Atlantic University has received a $4.5 million grant from the US Air Force to establish a high-fidelity platform for autonomous decision-making and real-time sensor fusion research. The T-1A Jayhawk simulator will be used to study cognitive performance, situational awareness, stress and decision-making under pressure.
Researchers developed new backtesting approaches for expectile forecasts to address issues with existing methods. The new tests disentangle unconditional coverage and independence properties, improving size and power properties.
Researchers developed a new method to predict extreme rainfall in Japan, using Integrated Nested Laplace Approximation - Stochastic Partial Differential Equation (INLA-SPDE), which outperformed traditional kriging methods. The study used hourly precipitation data from 752 meteorological stations across four main islands of Japan and fo...
A digital clinical decision support algorithm substantially reduced antibiotic prescribing in pediatric outpatient care in Rwanda, according to a non-randomized controlled trial. The study found no compromise in clinical recovery, suggesting the algorithm's effectiveness in guiding appropriate treatment.
A team from MIT and UC San Diego has developed a new method to uncover hidden biases, moods, and abstract concepts in large language models (LLMs). The approach identifies these connections within the model and allows for manipulation of the concept in generated answers.
Researchers from ISTA developed an algorithm that can extract and analyze information from the world’s most extensive biobank with unprecedented accuracy and speed. The method, dubbed gVAMP, enhances the framework's ability to extract complex information from the dataset at hand, providing a detailed overview of the effects on a trait ...
Researchers at UCSF and Wayne State University found that generative AI tools can perform orders of magnitude faster than human teams in analyzing health data. Junior researchers paired with AI generated viable prediction models in minutes, outperforming experienced programmers in hours or days.
A new study from the University of Rochester found that social media algorithms can reinforce echo chambers, but introducing randomness can help reduce this effect. By exposing users to a broader range of perspectives, algorithms can weaken feedback loops and promote more open-minded views.
A new study published in Risk Sciences investigates the potential of big data and modern predictor-selection methods to improve health insurance risk assessment. The analysis reveals that adding big data, particularly from smartphone use, improves out-of-sample prediction compared to traditional underwriting information.
A research team from Osaka Metropolitan University found that optimizing window-to-wall ratio and insulation can reduce energy consumption by up to 27% in subtropical regions. The study provides tailored design guidelines for each climate zone, promoting net-zero energy housing and climate adaptation policies.
A study investigates how interpretable deep learning models can explain neural network predictions for the Heston model. Global methods like Shapley values outperform local methods in producing financially intuitive explanations.
A new study uses large language models and network analysis to quantify investor emotions' fragility. High emotional vulnerability is linked to unstable stock prices and lower returns.
A team from Chongqing Medical University developed a model that integrates sMRI and fMRI data to predict treatment responders, achieving high accuracy rates. The dual-branch graph attention network (DBGAN) captures coordinated brain changes linked to emotion regulation and memory processing.
Researchers propose a novel approach to AI hardware design by integrating neuromorphic systems and compute-in-memory techniques to overcome the limitations of modern computing hardware. This could lead to more efficient data center energy use and enable real-time intelligence in compact, power-constrained systems.
New research found that Zestimate boosts efficiency in the residential real estate sector while benefiting lower-income neighborhoods. The algorithm helps alleviate uncertainty about property values, leading to increased buyer surplus and seller profit by an average of 5.94% and 4.36%, respectively.
A study published in The Journal of Finance and Data Science shows that integrating credit and debit data enhances the ability to predict credit card delinquency. By analyzing transaction patterns, the model identifies distinct behavioral segments with different risk profiles.
A new study identifies a practical and powerful prognostic marker that can help clinicians tailor treatment strategies to improve survival for patients with aggressive T-cell lymphoma. Researchers found that patients who relapse within 12 months of initial therapy have a dramatically declining chance of survival, making targeted therap...
Researchers developed instance-adaptive scaling framework that uses process reward model to estimate difficulty of question, enabling LLMs to spend more compute on promising solution paths. This approach achieves comparable accuracy with existing methods while reducing computational cost by up to one-half.
A new tool reorders content to downrank posts containing antidemocratic attitudes and partisan animosity, reducing polarization. Users who had such content downranked showed more positive views of the opposing party, with a noticeable impact on their emotional responses.
A study suggests personalized algorithms can lead to inaccurate generalizations, even when users know nothing about a topic. Researchers found that algorithmic personalization can bias learning and cause overconfidence in incorrect answers.
A new algorithm developed by researchers at North Carolina State University can optimize the movement of robotic prosthetic devices and help users exhibit a more natural walking pattern. The algorithm improves hip range of motion and reduces lower back pain for all five study participants.
A new study uses graph representation learning to enhance bookkeeper decision support for bank reconciliation. The method significantly improves match accuracy, especially on one-to-many matches, by leveraging a network of historical records and transactions.
The algorithm identifies the minimum set of locations where field studies would guarantee finding the least expensive route, considering problem structure and uncertainty. This method can be applied to broad classes of structured decision-making problems under uncertainty.
Professor Marko Huhtanen's research introduces a new method for compressing images by leveraging the best features of multiple well-known compression methods. The technique enables the removal of rigidity in traditional approaches, allowing for more precise control and adjustment during compression.
University of Rochester researchers developed algorithms to analyze complex chemistry in propane-to-propylene conversion. The study reveals the importance of defective metal sites and oxide phase stability in catalysts.
The new tool, called FSNet, combines machine learning and optimization to find feasible solutions quickly while ensuring constraints are met. It can unravel complex problems several times faster than traditional solvers and even outperform pure machine learning approaches.
Scientists at TU Delft developed an algorithm allowing multiple autonomous drones to work together to control and transport heavy payloads even in harsh weather. The system enables drones to lift and orient a payload with precision, ideal for reaching infrastructure like offshore wind turbines.
Researchers from The University of Osaka developed MicroAdapt, a groundbreaking self-evolving edge AI technology that enables real-time learning and forecasting capabilities within compact devices. It achieves up to 100,000 times faster processing and 60% higher accuracy compared to state-of-the-art deep learning methods.
A team of researchers has developed a new machine called the Electronic Probe Computer (EPC60) that can solve NP-complete problems, including optimal routing, scheduling, and network design. The EPC60 outperforms leading commercial software solvers in solving complex problems with high accuracy and efficiency.
Researchers found that the brain's logical reasoning network was active before learning to code, and continued to engage strongly after acquiring Python skills. This suggests that humans can repurpose cognitive areas involved in reasoning to learn computer programming.
VFF-Net applies label-wise noise labelling, cosine similarity-based contrastive loss, and layer grouping to improve image classification performance compared to conventional forward-forward networks. The algorithm reduces test errors on various datasets, enabling lighter and more brain-like training methods that make AI more sustainable.
Researchers from MIT and the MIT-IBM Watson AI Lab have introduced a new training method that enables vision-language models to localize personalized objects in a scene. By using carefully prepared video-tracking data with contextual clues, the model is better able to identify the location of a specific object in a new image.
A novel AI optimization model called GAN-Solar has been developed to address the technical bottleneck of accurate short-term solar forecasting. The model achieves significant improvements on key metrics compared to existing advanced models, producing high-definition forecasts that capture crucial details.
Researchers developed a more accurate cough-detection model using wearable health monitors' audio and movement data. The new model can distinguish between coughs and nonverbal sounds, improving the accuracy of respiratory disease tracking.
A team of researchers from Worcester Polytechnic Institute has developed a new approach to producing hydrogen using plasma technology and metal alloys. The method reduces energy consumption and carbon emissions compared to traditional methods, making it more environmentally friendly and potentially affordable.
A team of researchers developed a computational method that can design intrinsically disordered proteins with desired properties. The work uses automatic differentiation to optimize protein sequences and leverages molecular dynamics simulations for precision. This breakthrough has the potential to reveal new insights into diseases like...
MIT researchers developed a new framework that helps engineers design complex systems explicitly accounting for uncertainty. The framework allows them to model the performance tradeoffs of a device with many interconnected parts, each of which could behave in unpredictable ways. This approach can help engineers develop complex systems ...
Researchers developed an advanced survey system combining AR glasses with a camera and AI to objectively assess leafminer damage in the field. The DeepLab-Leafminer model achieved superior segmentation performance, resulting in high diagnosis accuracy rates.
Researchers at CARS create detailed maps of chemical reactivity, discovering regions of unexpected outcomes and reconstructing intricate reaction networks. This new understanding enables control over the formation of different major products from a set of starting materials.
A recent study from Spain aims to develop inclusive medical AI applications by overcoming binary logics and including the needs of trans people. The research team, led by Nataly Buslon Valdez, collaborated with PRISMA association to design AI apps that promote personalized treatments tailored to individual needs.
The SAGEST Predictive Simulation Center will develop simulation tools to give scientists confidence in exploring extreme physical conditions. The center, led by UVA's Xinfeng Gao, will use high-fidelity and low-fidelity solvers to balance accuracy and efficiency in predictions.
Researchers developed an AI-driven algorithm that can predict nearly 70% of hot flashes before they're felt. The Embr Wave wearable device will incorporate this technology to mitigate symptoms and provide meaningful relief.
A new algorithm developed by Núria Malats and her team can accurately predict the presence of metastasis in pancreatic cancer using medical images. The PMPD algorithm has shown promising results, classifying 56% of metastases with high accuracy and potentially avoiding unnecessary surgeries.
Researchers developed computational tools to analyze ecological data, identifying functionally equivalent species across different ecosystems. These tools use optimal transport distances to compare network structures, allowing for large-scale monitoring of ecosystem health and guiding conservation efforts.
Researchers at Purdue University have developed an algorithm that recovers detailed spectral information from photographs taken by conventional cameras. The method uses computer vision, color science, and optical spectroscopy to achieve high spectral resolution comparable to scientific spectrometers.
A study of 40,000 users over 15 years found that musical taste becomes more refined and personal with age, with younger listeners exploring a wide range of contemporary music and older listeners returning to their youth favorites. Nostalgia is a strong driving force in middle age and beyond.
The use of AI in the criminal justice system raises concerns about fairness and transparency. Researchers advise for clear understanding of data used and procedure by judges to use guidance from AI systems. Explainable AI systems may help, but transparency doesn't have to mean understanding computer code.
Researchers at Seoul National University of Science & Technology developed LWMalloc, a lightweight and high-performance dynamic memory allocator for resource-constrained environments. The new allocator outperforms ptmalloc by achieving up to 53% faster execution time and 23% lower memory usage.
ConcreteSC technology achieves significant speed boosts and improved efficiency in next-generation wireless networks. The innovation integrates user tasks into communication processes, reducing computational complexity and increasing semantic meaning.
Simpler, physics-based models can generate more accurate predictions than state-of-the-art deep-learning models for certain climate scenarios. However, simple models are more accurate when estimating regional surface temperatures, while deep-learning approaches excel at local rainfall estimation.
Researchers developed Vesalius to interpret complex data on cancer cell interactions, leading to potential discoveries in treating hard-to-treat cancers. The tool analyzes whole tissue architecture to identify predictive biomarkers and inform treatment options based on individual disease types.
A new study suggests that adopting AI in high-stakes settings like hospitals and airplanes requires evaluating algorithms and human decision-making simultaneously. The study found that accurate AI predictions improved participant performance by 50-60%, but inaccurate predictions led to a 100% degradation in proper decision making.
A research team developed a new method to precisely edit DNA by combining genetic engineering with artificial intelligence. The technique enables accurate modeling of human diseases and lays the groundwork for next-generation gene therapies.
Researchers at the University of Graz and the University of California, San Diego have developed a novel method to determine omega positions of lipids in complex biological samples. This breakthrough enables the study of biological mechanisms in unprecedented detail, particularly for inflammation-related diseases.
Researchers are combining machine learning algorithms with neuromorphic hardware to build brain-like devices that can learn from data and adapt in real-time. These devices have the potential to revolutionize industries such as manufacturing by enabling machines to sense their environment, adapt to new tasks, and make decisions without ...
A large-scale modeling study led by MIT researchers reveals that dynamically adjusting vehicle speeds can cut annual city-wide intersection carbon emissions by 11-22%. Implementing eco-driving measures could also result in a 25-50% reduction in CO2 emissions if only 10% of vehicles adopt the technology.
Researchers designed a novel transmitter chip that significantly improves energy efficiency in wireless communications. The compact, flexible system employs a unique modulation scheme to encode digital data into a wireless signal, reducing error and leading to more reliable communications.
A new study by MIT researchers introduces a method for machine learning with symmetry that is provably efficient, clarifying a foundational question in the field. This approach could lead to more powerful AI models designed to handle symmetry, benefiting applications such as drug discovery and materials science.
MIT researchers developed a fully autonomous experimental platform that can efficiently identify optimal polymer blends. The system uses a genetic algorithm to explore a wide range of potential combinations and autonomously identifies hundreds of blends that outperform their constituent polymers. This workflow could lead to advancement...