Articles tagged with Algorithms
Researchers at Pohang University of Science & Technology have developed a novel analog hardware using ECRAM devices that maximizes AI computational performance. Their technique, which uses a three-terminal structure with separate paths for reading and writing data, demonstrates excellent electrical and switching characteristics.
The University of Leicester is developing a method to shrink artificial intelligence algorithms for smarter spacecraft. The REALM project aims to demonstrate streamlined machine learning algorithms suitable for limited spacecraft power and computing performance.
A machine learning algorithm was trained to predict individuals with functional neurological disorder (FND) by analyzing their brain structure. The algorithm achieved significant above-chance accuracy in classifying FND participants against healthy controls and psychiatric samples, highlighting the importance of considering both brain ...
Researchers found that large language models perform poorly in high-stakes situations despite being better than smaller models, due to misalignment with human generalization function. Human generalization, which involves forming beliefs about others' abilities, plays a significant role in LLM performance and deployment.
A new position paper argues that single race-agnostic FRAX models would unfairly discriminate against Black, Asian, and Hispanic communities. The authors recommend retention of ethnic and race-specific FRAX models for the US with updated data on fracture and death hazards.
Researchers at USC developed a new method to accurately predict wildfire spread using satellite data and artificial intelligence. The model offers a potential breakthrough in wildfire management and emergency response, providing more precise and timely data for firefighters and evacuation teams battling wildfires.
A study found that large language models, despite accuracy in medical exams, fail to consistently request necessary examinations and often deviate from treatment guidelines. In comparison to human doctors, AI diagnoses achieved lower accuracy rates, highlighting concerns about their suitability for everyday clinical practice.
A new study reveals significant discrepancies in the effectiveness of political ads on Facebook and Instagram, favoring more extremist groups. Over 70% of parties used user profiling in their ads, and the far-right AfD proved to be the most effective, with ads almost six times more efficient than competitors.
A team of UCSF specialists predicts 24-hour seizure risk using brain activity patterns that foreshadow seizures. The discovery may improve quality of life for 2.9 million Americans living with epilepsy.
Researchers developed a method to assess the reliability of foundation models, enabling users to choose the best model for their task without testing it on real-world data. The approach measures consensus among multiple models and aligns representations to compare consistency.
Scientists from Trinity College Dublin created a computer program that visualizes molecular structure in the style of Piet Mondrian. The program uses blocks of color to represent symmetry and shape, making it easier to understand complex molecular interactions.
GenSQL integrates a tabular dataset and a generative probabilistic AI model to analyze complex tabular data. It can detect anomalies, predict outcomes, and generate synthetic data with just a few keystrokes.
Researchers developed an AI model that can estimate lung function from chest radiographs with high accuracy, potentially expanding options for pulmonary function assessment in patients who have difficulty performing spirometry. The study found a remarkably high agreement rate between the AI model's estimates and actual spirometric data.
A new machine learning-based method uses 3D structure of protein backbone with large language models to predict molecular changes that lead to better antibody drugs. The approach resulted in a 25-fold improvement against a virus, outperforming traditional methods that rely on generating huge amounts of data about protein sequences.
The SDC-DeepLabv3+ algorithm achieved a mean pixel accuracy of 95.84% and mean intersection over union of 96.87%, reducing background interference and enhancing filament visibility. The method shows potential for improved harvesting robot performance and precise filament harvesting.
A study found that large language models (LLMs) like ChatGPT underperform state-of-the-art detectors but can explain their analysis in plain language. LLMs' semantic knowledge makes them well-suited for detecting deepfakes, providing a common sense understanding of reality.
Researchers developed an AI model that accurately predicts metal yield strength by combining physical theory with machine learning. The model outperforms traditional methods, which often rely on extensive experimentation.
Researchers at NTNU have developed a way to reconstruct 3D models of the colon using single images taken by capsule endoscopy cameras. These models can aid in detecting abnormalities and signs of disease, enabling specialists to make diagnoses faster.
A new study found that animals use a wide range of strategies to accomplish tasks, many of which are just as effective as the optimal solution but require less brain power. The research provides a theoretical framework for understanding these 'good enough' strategies and their potential applications in animal behavior.
Researchers at Bar-Ilan University have discovered a new scaling law that governs how artificial neural networks handle an increasing number of categories for identification. This law reveals how the identification error rate increases with the number of required recognizable objects, impacting AI latency and efficiency.
A team of researchers created an advanced method for automatic microfossil detection and analysis using AI. The method has shown great potential in utilizing AI to analyze vast amounts of microfossil data, potentially helping geologists better utilize wellbore samples.
Researchers developed two clinical ageing clocks, PCAge and LinAge, that use blood tests, a urine test, and a health questionnaire to estimate future mortality risk. These clocks show significant predictive efficacy in characterising individual future ageing trajectories.
The University of Texas at Arlington has awarded over $130,000 to support new research projects through its Research Enhancement Program grants. The funding is intended to provide seed money for researchers to initiate creative endeavors and make a positive impact on society.
A new computer vision technique developed by MIT engineers significantly speeds up the characterization of newly synthesized electronic materials. The technique automatically analyzes images of printed semiconducting samples and quickly estimates two key electronic properties: band gap and stability.
Researchers developed a reliable iris recognition method by applying statistical limits to the spatial domain zero crossing technique, reducing errors to 0.022%. The algorithm uses a neural network to recognize unique features of each person's iris, achieving over 99.78% accuracy.
Researchers from Mass General Brigham created an algorithm to generate personalized DBS treatment plans based on four major PD symptoms. The algorithm improved patients' symptoms in four of five cases, demonstrating its potential to improve treatment beyond standard-of-care approaches.
A new study from Chalmers University of Technology shows that AI-controlled charging stations can offer personalized prices to electric vehicle users, minimizing both price and waiting time. However, the researchers highlight the importance of addressing ethical issues related to data exploitation by motorists.
Researchers Dr. Samson Zhou and Dr. David P. Woodruff aim to create secure algorithms for big data models using mathematical connections and cryptography ideas. They focus on streaming models, which process data in real-time, and address challenges such as randomness and different types of attacks.
Researchers at Texas A&M University have developed a more accurate method for tracking reservoir evaporation rates, accounting for factors not considered by current methods. The new algorithm reveals a clear geographic distribution and strong seasonality of evaporation throughout Texas.
A study found that thumbnails on a popular video-sharing platform used by children contained attention-capturing designs with violent and stereotyped themes. The researchers aim to understand how children respond to such thumbnail designs and their impact on the quality of consumed content.
A new algorithm called adaptive intersection maximization (AIM) removes high-frequency noise from super-resolution optical microscope data in real time, achieving sub-nanometer precision. This allows scientists to study chemical and biological systems far more easily and precisely than before.
Researchers have developed a system combining bio-inspired cameras with AI to quickly detect obstacles around cars, using less computational power. The hybrid system detects objects up to one hundred times faster than current systems while reducing data transmission and processing needs.
A breakthrough in trauma care has nearly quadrupled the 'golden hour' for treating large animals with internal bleeding during emergency ground and air transport. Researchers used a closed-loop, autonomous intervention system to resuscitate pigs with traumatic injuries, extending their survival time by several hours.
A new computer algorithm has been developed to enhance the management of invasive species globally, optimizing resource allocation and reducing costs. The innovative tool is adaptable to various population dynamical models and treatment methods, improving the effectiveness of environmental conservation efforts.
The team aims to create a system that can deliver items without human contact, using cables, knots, and multiple robots. They will focus on scaling up the transport of small objects like a basketball and solar panel.
Researchers have developed an AI algorithm that can track protein clumping under the microscope in real-time, revolutionizing the study of neurodegenerative disorders. The tool helps identify key characteristics of clumped proteins, which can lead to new therapies.
Engineers developed a material that mimics human bone for orthopedic femur restoration, providing optimized support and protection from external forces. This innovative approach uses machine learning, optimization, and 3D printing to create a fully controllable computational framework.
A UCF researcher is developing algorithms to track space objects and predict their orbits, which will also aid in maritime domain awareness. The computational framework will enable spacecraft to operate autonomously without intervention from Earth.
Researchers developed a multimodal algorithm for improved sarcasm detection, examining multiple aspects of audio recordings for increased accuracy. The approach combines sentiment analysis using text and emotion recognition using audio for a comprehensive analysis.
Researchers used AI to detect localized versus advanced cancer stages in oropharyngeal squamous cell carcinoma patients. The study suggests improved patient care and clinical decision-making through AI-assisted health record extraction.
A new approach uses neural networks to automatically determine polynomial coefficients for digital pre-distortion (DPD) in RF-PAs, reducing hardware complexity and power efficiency. This method can correct non-linearities and support emerging standards without extensive real-time processing.
Researchers created a digital twin model that predicts and controls complex systems, achieving higher accuracy than traditional methods. The algorithm is compact, energy-efficient, and easy to implement, making it suitable for self-driving vehicles and other dynamic systems.
Researchers developed a new AI algorithm called Maximum Diffusion Reinforcement Learning (MaxDiff RL) to improve robot reliability. The algorithm enables robots to learn complex skills more efficiently by encouraging exploration of their environments.
Researchers designed a new AI model that accurately predicts treatment outcomes for stroke prevention in people with heart disease. The model, called CURE, was trained on a massive dataset of de-identified patient information and fine-tuned to focus on specific health conditions and treatments.
Researchers at Aalto University have created a method to manipulate objects with wind, allowing for controlled movement in different directions. The technique uses an airflow field to move objects along desired trajectories, with potential applications in robotics and complex processing tasks.
The American Heart Association has funded four new grants to evaluate the use of race in predicting heart disease risk and develop unbiased clinical algorithms. The research projects will test various risk models to remove racial bias from care decisions, improving patient outcomes.
Researchers developed an algorithm that uses stochastic differential equations to identify key data points in noisy data, improving early warning signal accuracy. The approach can be applied to various domains, including ecology and mental health, and has been validated through simulations.
Researchers from Drexel University have developed a machine-learning algorithm that can extract and recognize digital fingerprints of AI-generated video, including Stable Video Diffusion, Video-Crafter, and Cog-Video. The algorithm can be trained on just a few examples of new AI generators to detect them.
A new framework mitigates bias in machine classification by evaluating under different fairness metrics and inferring specific bias terms from data. The framework substantially reduces bias in classification outcomes while preserving accuracy across seven datasets and 21 classifiers.
A Concordia PhD candidate argues that tech companies' opaque algorithms for storing and sharing personal content raise questions about selection and representation. An Apple update blocking Holocaust-themed photos in the iPhone Photos app's Memories sparked a debate among users, with some expressing annoyance at being told what to see.
A team of researchers at MIT has developed a new method to model the irrational behavior of humans, which can be used to predict their future actions. By analyzing an agent's previous decisions, the technique infers its computational constraints and adapts to human collaborators' weaknesses.
Researchers at Bar-Ilan University developed a new AI confidence measure that distinguishes between high- and low-confidence decision making in deep learning architectures. This breakthrough enables the creation of safer and more reliable autonomous vehicles by prioritizing human intervention when confidence levels are lower.
The project, AI STORIES, aims to understand how artificial intelligence creates new narratives and challenges dominant American narratives. The research will test the hypothesis that AI-generated responses mimic sci-fi stories where AI gains human emotions.
A new machine-learning approach outperforms human testers in generating diverse prompts that trigger a wider range of undesirable responses from chatbots. The technique provides a faster and more effective way to ensure the safety of large language models.
A new study by Carey Morewedge and colleagues found that people are more likely to recognize bias in algorithmic decisions than their own. This is because algorithms can codify and amplify human bias, but also reveal structural biases in society. The research suggests ways to increase awareness of biases and correct them.
Researchers propose a hybrid data driven framework combining VMD, ISSA, and MKSVR to enhance battery SOH estimation and RUL prediction. The approach achieves accurate predictions with high stability.
Recent advancements in AI and IoT have improved earthquake prediction by identifying patterns in historical seismic data. However, limitations such as computational complexity, data quality, and interpretability remain, requiring a comprehensive approach to integrate diverse datasets.
Researchers have developed a new open-source algorithm called Conditional Variational Diffusion Model (CVDM) that improves the quality of images by reconstructing them from randomness. The CVDM is computationally less expensive than established diffusion models and can be easily adapted for various applications.
Researchers from the University of Washington created an AI algorithm to analyze infant poses using limited training data. By leveraging generative AI, they were able to produce high-quality results, enabling parents to monitor their babies' daily activities and detect potential health issues early.
A new MIT-derived algorithm corrects coarse climate model predictions by 'nudging' them toward more realistic patterns, leading to more accurate forecasts of extreme weather events. The approach uses machine learning and dynamical systems theory to improve the resolution of large-scale climate models.