Articles tagged with Algorithms
A study by MIT researchers found that algorithmic monoculture, like using a single algorithm in hiring, can create informational echo chambers that hinder exploration. However, bundling multiple algorithms together can overcome this limitation, enabling monoculture to perform as well as or better than a polyculture.
University of Tennessee researchers are part of a nearly $1 million project to build a smart food production network, optimizing locally grown food distribution. The project, called DFARM, employs a user-centered software platform to connect community gardens, indoor hydroponic systems, and local food hubs.
Researchers developed an AI system, Ataraxos, that excels at Stratego, a two-player game of imperfect information, by combining efficient training algorithms with new techniques for calculated decision-making. The system defeated top human players and outperformed other models in strategic games, demonstrating its potential to help hum...
Robert Loredo's new book, 'Quantum Readiness for Leaders,' offers a practical guide for business and technology leaders to navigate the opportunities and challenges of quantum technology. The book provides frameworks for evaluating quantum opportunities, building quantum teams, and preparing security architectures.
Researchers from Tokyo Metropolitan University used evolutionary algorithms to identify optimal shapes for ultra-thin, bio-inspired corrugated airfoils. They discovered designs that minimized drag or maximized lift, with corrugations near the leading-edge reducing frictional drag and convex shapes near the trailing edge increasing lift.
MIT researchers developed a new technique to help generative AI models meet strict safety requirements without sacrificing output quality. The 'HardFlow' algorithm reformulates hard-constrained sampling as a trajectory-optimization problem, enabling subtle corrections while enforcing hard constraints.
Researchers at USC developed an algorithmic tool to improve screening of patients for Alzheimer's clinical trials, reducing unnecessary PET scans by over half. The tool uses blood plasma biomarkers to identify patients at risk of Alzheimer's disease, enabling faster and more efficient recruitment for trials like AHEAD 3-45.
A quantum LDPC code with a large minimum distance near 48 protects 4,612 logical qubits using 9,216 physical qubits, reducing hardware overhead. The code's design framework, inspired by classical LDPC codes, allows for strong evidence of a minimum distance and threshold phenomenon.
A cross-sectional study suggests that seeking emotional support from generative AI is a unique marker of psychological distress in children and adolescents. Clinical frameworks and digital literacy programs should distinguish between functional AI assistance and algorithmic interfaces as a digital refuge for emotional needs.
Researchers found that personalized content from social media algorithms can negatively impact users' mental health by reinforcing negative emotions and thoughts. The study suggests that educating people on critical social media engagement behaviors can help mitigate this effect.
Researchers found that sector labels in the S&P 500 only partially capture a company's financial situation. The study used machine learning models to group firms by financial similarity, revealing nine economically interpretable clusters that generally showed lower internal dispersion than conventional sectors.
Researchers Vincent Vatter and Robert Brignall found 55 times out of 1096 daily puzzles, the game 'lied' about high scores, with an average error of 2 points. They computed the true high score for every possible combination using a novel approach to reduce the apparent complexity of the problem.
Researchers found that smaller networks can be Pareto optimal, where no bank can be made better off without making another worse off. The study suggests that larger networks can lead to free-riding, reducing expected profits and market efficiency.
Researchers develop a hybrid path-finding model for drones, finding that 2-3 rounds of adaptive planning are sufficient for optimal results. This approach balances speed and efficiency, making it practical for various applications, including search-and-rescue operations and commercial agriculture.
MIT engineers develop a tool that generates plausible extreme events and worst-case scenarios without relying on extreme data, enabling planners to prepare for unprecedented scenarios. The algorithm takes a statistical approach to learn from available data, excluding implausible weather scenarios, and projects how extreme events might ...
A new brain-inspired algorithm, Spi-Fly, demonstrates promise for achieving practical applications in scent classification, particularly in scenarios with limited training data. The algorithm shows accurate classification of scents and can learn with few-shot and continual learning methods, making it suitable for real-world applications.
A new navigation system for cyborg insects combines AI-based real-time terrain recognition with the cockroach's natural climbing ability, enabling faster and more efficient navigation. This approach allows the insects to traverse obstacles, climb walls, and cross holes with reduced detours and steering stimulation.
Petroleum science is at a historic turning point, facing 100 grand challenges that span six domains. The challenges include low-carbon transition, AI-driven solutions, and interdisciplinary collaboration to address issues like CCUS, geothermal energy, and hydrogen energy.
UniSpec delivers lossless LLM acceleration without retraining while adapting automatically to different hardware platforms and multilingual workloads. The framework achieves up to 2.6× faster inference than existing methods across multiple models, hardware, and languages.
Researchers have developed a numerical model that explains how increasing ultrasonic power reduces chemical reaction rates in sonochemistry. The model shows that oscillating bubbles emit their own sound waves, generating unwanted noise that distorts the ultrasonic field and limits reaction efficiency.
A new mathematical approach using optics helps computers solve larger, more complex optimization problems by reducing computational demands. The framework can be applied to various real-world challenges, including facility placement and data clustering, with potential benefits for a carbon-neutral future.
USC researchers have been selected for the U.S. Department of Energy's Genesis Mission to harness artificial intelligence for scientific discovery and innovation. Two projects led by USC will explore ways to develop faster and more energy-efficient computing hardware and better understand the natural concentration of critical minerals.
A Chinese research team has proposed a novel approach to separate dimethyl carbonate from methanol using a tailor-made ionic liquid and heat pump-assisted distillation. The method significantly reduces energy consumption and costs compared to traditional methods, making it an attractive solution for the chemical industry.
Twenty NII papers were accepted at ACL 2026, including two that won the Best Theme Paper and Outstanding Paper awards. These achievements lay the groundwork for developing trustworthy AI and advancing research in explainability and transparent AI.
Researchers developed an AI that navigates ships using human-like decision-making, handling complex situations such as narrow channels and multiple vessels. The AI achieved compliance with maritime traffic rules and demonstrated unexpected behavior like local navigation customs.
A team of researchers used UAV-mounted LiDAR sensors to collect point cloud data from over 270 maize doubled haploid lines. They found that single-plant-scale estimation accuracy was superior to row-scale estimation, with higher R² values and lower RMSE values.
A team of researchers from the University of Cambridge and UC Santa Barbara developed 'adversarial' mathematical systems to map out where AI prediction breaks down. They identified two main reasons why machine learning fails: algorithmic limitations and hidden patterns in complex systems.
Researchers developed an auditing technique to test generative AI models for malicious capabilities, without prompting them for illegal outputs. The new approach identifies model variations that can generate CSAM with 100% accuracy.
Using the YEARS diagnostic algorithm is as safe as computed tomographic pulmonary angiography (CTPA) for diagnosing suspected pulmonary embolism in patients with cancer. This strategy saved 22% of patients from undergoing CTPA, highlighting its potential to streamline cancer care.
A new framework introduced by Dr. Patricia Ning's algorithm strengthens local, community-based surveillance capacity in all regions, anticipating future pandemics. The Iterative Block Particle Filter algorithm optimizes resources for early detection of viral variants, reducing the time between detecting a disease variant and sequencing...
Researchers developed a new system called Murakkab to optimize agentic workflows for AI applications. It enables developers to describe their intent in high-level terms, automating the selection of models and tools to use, and optimizing hardware configurations in real-time.
MIT researchers developed a framework that allows users to apply constraints to algorithmically generated structures, making them more buildable. The approach has the potential to reduce carbon emissions in construction by up to 90% by designing structures with multiple materials and taking into account materials' properties.
A team of neuroscientists highlights the distinction between intelligence and consciousness, warning against confusing AI systems with human-like emotions and experiences. Decades of research support their argument, citing examples like blindsight, which demonstrates intelligent behavior without conscious experience.
The Association for Computing Machinery (ACM) will publish Theory and Practice of Logic Programming (TPLP), an international venue for refereed papers on logic programming. The entire TPLP archive dating back to 2001 will be openly accessible via the ACM Digital Library.
The study proposes a three-stage maturity model for AI integration in pathology, highlighting key barriers to clinical translation: data fragility, workflow misalignment, and institutional trust deficits. Infrastructure-first AI, workflow-embedded intelligence, and adaptive governance are proposed pathways toward sustainable clinical i...
A study found that south-facing green walls can improve indoor thermal conditions by up to 1.7°C and enhance outdoor thermal comfort through albedo effects. Low albedo exterior surfaces also improved outdoor thermal comfort by approximately 1.5°C, while high albedo surfaces reduced indoor temperatures.
A team of researchers from The University of Osaka has developed a new approach for depth reconstruction from defocus, estimating distances by analyzing blur in an image. Their method combines a coded-aperture camera with diffusion-model-based AI to accurately estimate depth and produce high-quality images.
Researchers have developed a wearable sensor that reads chemical signatures of human breath to decode silent speech into text. The device uses a microscopic nanoforest to capture rapid water vapor changes, achieving 98.51% accuracy rate.
Researchers developed an Insect Synergy Circuit that integrates body movement and internal physiological information to guide insect navigation. The system achieved high accuracy in classifying environmental conditions, enabling gentle control over the insect's movements.
A new DGMoE framework enhances EEG-based emotion recognition by modeling individual differences, achieving high accuracy rates on public datasets. The framework's two-stage selection mechanism and graph-convolution-based expert modules improve robustness and generalization to unseen subjects.
A new open-source trajectory-planning system, MIGHTY, has been developed by researchers at MIT and the University of Pennsylvania. The system enables robots to generate smooth flight paths while reacting to obstacles in real-time, making it suitable for applications such as search-and-rescue, last-mile delivery, and industrial inspection.
A team of researchers proposes a deep learning architecture called CCDNN to learn correlated representations for multi-source data fusion. The method demonstrates promising performance, surpassing existing methods in reconstruction tasks and achieving better results in industrial fault diagnosis and remaining useful life cases.
A study of medical residency matching found that unequal outcomes emerge from differences in how applicants seek information, interpret advice, and understand the system. Researchers recommend investing in better explanation, training, and support for users to mitigate inequality.
A team of researchers has developed a way to precisely move tens of thousands of individual atoms within a material in minutes at room temperature. This approach uses algorithms to carefully position an electron beam and scan the beam to drive atomic motions, enabling the creation of defects with tunable functions.
A team of scientists has found a naturally occurring Voronoi pattern in the Chinese money plant, which helps explain how plants create complex patterns on their leaves. This discovery sheds light on how plants solve problems in nature and may provide new insights into the math underlying evolution and development.
A new AI framework, Hi4GS, revolutionizes wheat yield prediction by improving accuracy and identifying key genetic markers. The framework streamlines high-dimensional genotypic data, uncovering the actual genes influencing yield.
Researchers from MIT have developed a more user-friendly and efficient method to identify potential system failures in cloud computing algorithms. The 'MetaEase' technique analyzes an algorithm's source code directly to uncover hidden blind spots that might cause unexpected failures, reducing the risk of costly network outages.
Researchers from Universitat Rovira i Virgili developed an AI tool called CoCoGraph that can generate realistic molecules complying with chemistry laws. The system uses a diffusion model to create plausible structures, resulting in 100% chemically valid molecules, and has been found to be more realistic than other state-of-the-art models.
Researchers develop quantum algorithms to simulate polymer degradation caused by UV radiation, using industrially relevant aircraft coatings as an example. The goal is to optimize surface coatings for various industries, improving safety and reducing costs.
The partnership aims to generate evidence on the potential of MitoQ to slow or improve markers of biological ageing and support longevity. Mitochondria-targeted antioxidants like MitoQ are crucial in producing energy while reducing oxidative stress, a key contributor to ageing.
A new optimization framework helps food banks deliver food more efficiently by accounting for variables such as food availability and household demand. The tool has been incorporated into an app that can also be used by businesses to address delivery logistics challenges.
Flinders University experts caution that AI's impressive capabilities do not automatically translate into safe use for patients. The researchers stress the need for strong governance and clearer standards for evaluation to ensure AI supports doctors in busy care settings.
A recent study has developed an analytical model of downburst wind fields, which reproduces key observable features while adhering to fundamental mechanical principles. The model proposes a framework for assessing train overturning due to downbursts, with high train speeds identified as the most significant contributor to increased risk.
MIT researchers have created an 'EnergAIzer' method that generates reliable results in seconds, allowing data center operators to optimize resource allocation and reduce energy waste. The tool leverages patterns from AI workloads and software optimizations to provide fast but accurate power estimates.
Researchers developed Sandook, a software-based system that tackles three major sources of performance-hampering variability simultaneously. The two-tier architecture optimizes task distribution for the overall pool while faster schedulers on each SSD react to urgent events.
Researchers developed a novel flowering spectral index model, FI-R, for precise fine-scale extraction of angiosperms over large areas. The model achieved high accuracy and applicability across multiple multi-spectral sensor images, with overall accuracies exceeding 94%.
A new testing framework, Scalable Experimental Design for System-level Ethical Testing (SEED-SET), balances measurable outcomes and qualitative values like fairness. The system uses a large language model to capture stakeholder preferences and identifies scenarios where AI systems align with human values.
HeapGrasp uses RGB images to analyze object silhouettes and estimate its 3D shape, reducing the need for depth information. The approach achieves high accuracy while minimizing camera movement and execution time.
A new study developed an AI-driven strategy that accelerates catalyst discovery while revealing the underlying chemistry. The approach, referred to as 'gray-box,' provided meaningful insights into the effect of individual promoters and synergistic interactions between them.
Using a computational model, neuroscientists at MIT showed how the brain selectively focuses attention on one voice among others in a noisy environment. The model found that amplifying the activity of neural processing units that respond to features of a target voice allows that voice to be boosted to the forefront of attention.