Articles tagged with Deep Learning
The College of Engineering at Texas A&M is developing a suite of university-wide resources to integrate generative AI into course material, research, and outreach. The initiative aims to make generative AI a core part of the academic toolkit accessible to faculty across disciplines.
A new study in Nature Communications found that AI models exhibit a geometric property called convexity, which helps humans form and share concepts. Convexity is also linked to the performance of AI models on specific tasks.
The CNOP-DL method extends classical CNOP for deep learning methods, breaking deterministic causality and attributing forecast errors to all input slices. This new structure identifies critical time steps and locations where additional observations can significantly improve forecasts.
Researchers used machine learning to simulate galaxy evolution and supernova explosions, achieving speeds four times faster than supercomputers. This breakthrough enables the study of galaxy origins, including the creation of the Milky Way's elements essential for life.
A research team developed an AI-based classification system using InceptionResNetV2 and DenseNet121 to identify five types of facial pigmented lesions. The system demonstrated high diagnostic accuracies compared to board-certified and non-certified dermatologists, with potential as a diagnostic support tool for clinical practice.
A review of AI eye imaging devices approved for patient care found significant gaps in evidence, including lack of transparency on training data and limited diversity in clinical evaluations. The study highlights the importance of rigorous, transparent evidence and data to ensure equitable and effective AI-based solutions.
Researchers have developed a new method to physically restore original paintings using digitally constructed films that can be removed if desired. The process uses a polymer film mask printed on a very thin film and aligned to an original painting, which takes around 3.5 hours from start to finish.
Researchers used proactive and transfer learning strategies to mitigate data shifts in AI models for hospital applications. They found that models trained on one hospital type performed better than those trained on all hospitals using transfer learning.
Assistant professor of electrical and computer engineering Natasa Miskov-Zivanov is receiving a $581,503 NSF CAREER Award for her project that leverages AI to design more effective lymphocytes for cancer immunotherapies. The system aims to accelerate the process of designing new therapeutic cell designs.
A refined artificial intelligence (AI) tool has shown promise for objective evaluation of patients with facial palsy. The 'fine-tuned' model demonstrated substantially lower error rates and improved keypoint detection in every area of the face, including areas of asymmetry.
A new near-real-time prediction model for earthquake-triggered landslides has been developed, utilizing a global database of 398,698 mapped events and cutting-edge deep learning. The model achieves spatial accuracy exceeding 82% and can generate probability maps of landslide occurrence in under one minute.
A new deep learning model, ENDNet, significantly enhances subgraph matching accuracy by identifying and neutralizing extra nodes that interfere with the matching process. This improves performance in pattern recognition tasks across various fields, including drug discovery and natural language processing.
A new study from the University of Surrey explores how metaverse platforms like Roblox and ZEPETO are changing consumer engagement forever. Digital doppelgängers, using AR and VR technologies, create immersive experiences that enhance brand engagement and emotional connections.
A portable device can instantly detect dangerous street drugs at extremely low concentrations, highlighting their dangers. The device, being trialled by drug-checking services in the UK, Norway, and New Zealand, allows for cheap and on-the-spot analysis of substances.
A new microscopy method, LICONN, developed by ISTA scientists and Google Research, can reconstruct mammalian brain tissue with all synaptic connections between neurons. This technique uses standard light microscopes and hydrogel to achieve high resolution and opens up possibilities for visualizing complex molecular machinery.
Researchers developed a novel vote-based model for accurate hand-held object pose estimation, addressing issues with existing approaches. The new framework achieves significant improvements in accuracy and robustness, enabling robots to handle complex objects and advancing AR technologies.
A new review advocates for building confidence in AI applications by implementing robust data governance frameworks, enhancing transparency, and involving stakeholders. The authors emphasize the importance of addressing ethical implications and ensuring equitable access to AI-driven innovations in clinical oncology.
Recent high-quality deepfake videos can feature realistic heartbeats and minute changes in face color, making them challenging to detect. Researchers found that even small variations in skin tone and facial motion can replicate the original pulse in deepfakes.
Researchers from OIST and Hebrew University developed a novel method to measure energy usage during movement using video and 3D-tracking via deep learning. This innovative approach expands the study of movement energy in ecology, physiology, and beyond, enabling the accurate measurement of energy consumption in smaller animal species.
A new AI tool has been developed to predict the relapse of pediatric brain cancer with high accuracy, using temporal learning algorithms to analyze sequential brain scans. The tool achieved an accuracy of 75-89% in predicting recurrence, outperforming predictions based on single images.
Researchers developed an IEAC framework combining robust security with high-capacity transmission performance, achieving a record 1 Tb/s secure transmission over 1,200 km of optical fibre. The system eliminates the trade-off between security and speed by integrating encryption into the communication process.
A Lehigh University team developed a novel machine learning method to predict abnormal grain growth in materials, enabling the creation of stronger, more reliable materials. The model successfully predicted abnormal grain growth in 86% of cases, with predictions made up to 20% of the material's lifetime.
Dr. Latifur Khan, a renowned computer science professor, has been elected as an AAAS Fellow for his pioneering work in machine learning and big-data analytics. He developed innovative solutions to adapt machine learning models to cybersecurity risks and created an AI-driven tool to analyze political conflict and violence.
A new hardware platform for AI accelerators capable of handling significant workloads with reduced energy requirement has been developed. The platform leverages III-V compound semiconductors to create photonic integrated circuits, which operate at the speed of light with minimal energy loss.
Researchers developed a method to detect epileptic seizures in humans using canine EEG data. The approach leverages feature similarities across species and modalities, reducing input space discrepancies. Euclidean alignment and knowledge distillation are key components of the proposed joint alignment mechanism.
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.
Researchers have introduced Orion, a novel framework that brings fully homomorphic encryption to deep learning, enabling computations on encrypted data without decrypting it. The framework achieves a 2.38x speedup over existing state-of-the-art methods and enables high-resolution FHE object detection using large neural networks.
Researchers developed an AI model that classifies variable stars from light curves with high accuracy, outperforming traditional approaches. The StarWhisper LightCurve series achieves near 90% accuracy with minimal manual intervention, paving the way for parallel data analysis and multi-modal AI applications in astronomy.
The study systematically traces Generative AI evolution from deep learning to foundation models, highlighting four distinct stages and successful applications. Key challenges like safety concerns and theoretical breakthroughs require further attention and development in the field of Generative AI.
Researchers from Osaka Metropolitan University used a deep learning model to discover new bubble-like structures in the Milky Way galaxy, providing insights into star formation and galaxy evolution. The study also revealed shell-like structures formed by supernova explosions.
A research team at Kumamoto University developed a deep learning-based method for analyzing the cytoskeleton more accurately and efficiently than ever before. This technique enabled more reliable measurements of cytoskeleton density, which is critical for understanding cellular structure and function.
A new AI model developed by UC Riverside scholars combines historical sales data with economic demand theory to predict prices in uncertain times. The model retains high accuracy, demonstrating a substantial improvement over other methods in reducing generalization errors.
Researchers developed a deep learning algorithm to denoise ultra-low dose CT scans, improving image quality and accuracy. The study found that this approach can diagnose pneumonia in immunocompromised patients using only 2% of the radiation dose of standard CT scans.
Researchers developed single-shot super-resolved fringe projection profilometry (SSSR-FPP) using deep learning to achieve 100,000 frames-per-second 3D imaging. This breakthrough offers new insights into ultra-fast dynamic processes and could revolutionize fields like mechanics and biology.
A new study suggests that artificial intelligence can effectively detect wildfires in the Amazon rainforest, using satellite imaging and deep learning. The technology achieved a 93% success rate in training models via datasets of images with and without wildfires.
This study utilized deep learning models to diagnose and predict the likelihood of malignant transformation in oral potentially malignant disorders. AI-driven approaches offer noninvasive, cost-effective, and objective means to enhance early detection and improve patient outcomes.
A deep learning model, CGMformer, leverages large-scale continuous glucose monitoring (CGM) data to extract individual glucose dynamics. The model captures a continuous picture of glucose fluctuations, identifying patterns that may indicate early metabolic dysfunction.
A new deep learning approach called HikingTTE significantly improves hiking travel time estimation by considering individual walking ability and fatigue. The model outperformed conventional techniques, reducing Mean Absolute Percentage Error by 12.95 percentage points.
The National Center for Supercomputing Applications has been awarded a grant to continue its Research Experience for Undergraduates (REU) program, which provides students with hands-on experience in machine learning and deep learning projects. The program aims to develop open source machine learning models and tools and apply them to r...
Deep Nanometry enables high-speed analysis of nanoparticles, detecting even trace amounts of rare particles like extracellular vesicles indicative of colon cancer. This technique has potential applications in various fields including vaccine development and environmental monitoring.
Researchers developed an innovative model, ECG-LM, that leverages large language models to interpret complex ECG signals. The model improves the accuracy and speed of heart-related diagnostics, particularly in resource-limited environments.
A team of researchers from Sophia University demonstrated the potential of combining social media posts and transformer-based learning models to detect heat stroke risks. The study found that a Japanese model called LUKE achieved high accuracy, suggesting its viability in monitoring heat stroke risks during heat waves.
Researchers have created a geometric deep learning approach called MARBLE that can infer latent brain activity patterns across experimental subjects. The method uses dynamic motifs to analyze neural population recordings and has been shown to be more interpretable than other machine learning methods.
Researchers used deep learning models to compare gene regulation in different cell types of human and chicken brains, shedding new light on brain evolution and providing tools for studying gene regulation. The study found that while some cell types are highly conserved between birds and mammals, others have evolved differently.
A new deep learning technique called CTLESS enhances myocardial perfusion imaging accuracy without requiring additional radiation scans. This method leverages deep learning to estimate attenuation maps, improving diagnostic interpretation and potentially boosting technological health equality across the U.S. and worldwide.
Researchers develop a new framework called MSF-Net to improve WiFi-based human activity recognition, achieving high accuracy scores compared to state-of-the-art techniques. The technology has potential applications in smart homes, rehabilitation medicine, and care for the elderly.
Researchers have proposed a new fault-tolerant framework for distributed deep learning model training that minimizes overhead and improves efficiency. By utilizing idle system resources during training, the framework effectively coordinates tasks with fault-tolerance functions.
A recent study introduces an innovative method for analyzing body composition, providing accurate assessments of body fat and muscle distribution. The approach utilizes deep, nonlinear methods to enhance estimation accuracy, surpassing previous linear models.
A recent study by University of New Hampshire researchers explores how to standardize rock climbing route difficulty through machine learning techniques, aiming to promote inclusivity and accuracy. The most successful approach used route-centric natural language processing methods, achieving an accuracy of 84.7%.
A new deep learning model has been developed to detect and segment lung tumors on CT scans with high accuracy. The model achieved 92% sensitivity and 82% specificity in detecting lung tumors, outperforming physician-delineated volumes in some cases. However, further research is needed to improve the model's performance for larger tumors.
Researchers at Sharjah University have designed techniques to automatically predict suitable image dimensions using deep learning models. The proposed methods aim to bridge the gap in automating image retargeting approaches based on an image and target resolution.
The EPFL team has developed a deep-learning pipeline called MaSIF to design new proteins that interact with therapeutic targets. They have successfully designed novel protein binders that can recognize and bind to drug-protein complexes, offering potential applications in cell-based therapies and biosensors.
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 developed an AI-powered technology that transforms low-resolution, label-free images into high-resolution, virtually stained ones without fluorescent dyes. This innovation delivers stable and accurate cell visualization, overcoming limitations of traditional imaging methods.
Researchers at the University of Washington have developed new proteins that can neutralize lethal snake venom toxins using deep learning computational methods. These protein designs show promise for creating safer and more cost-effective antivenoms, potentially saving millions of lives annually.
Researchers have developed a new geometric machine learning method called MaSIF, which enables the design of proteins that bind specifically to desired molecular structures. This approach accelerates precision drug development by allowing for precise dosing and control of biological drugs.
Researchers developed an explainable deep learning model to predict and analyze HABs in Chinese lakes and reservoirs, achieving significant improvement over conventional machine learning methods. The model identified water temperature as the most influential factor driving algal bloom dynamics.
A novel method called RESQUE predicts computational and energy costs for updating deep learning/AI models, allowing users to make informed decisions about when to update models to improve AI sustainability. The researchers conducted extensive experiments to validate the performance of RESQUE.
Researchers developed TLE-PINN to predict melt pool morphology in selective laser melting, achieving superior accuracy and faster training times. The framework combines physics-informed constraints with deep learning techniques, enabling precise and efficient solutions for real-time process control and manufacturing optimization.
The one-core-neuron system (OCNS) minimizes model size while maintaining high performance in deep learning. OCNS employs a single neuron to encode high-dimensional data into a one-dimensional time-series representation, reducing parameters by an average of 0.035%.